MSc in Industrial and Applied Mathematics - Grenoble

Modélisation dynamique et estimation hybride pour la maintenance prédictive de systèmes tournants.

Wed, 04 Mar 2026 00:00:00 +0000

Intégrer l’Univers : Solveurs ODE Haute-Performance pour l’Astrophysique Numérique

Tue, 20 Jan 2026 00:00:00 +0000

Optimisation de forme en mécanique du contact Application à la régulation thermique d’équipements de satellite

Tue, 20 Jan 2026 00:00:00 +0000

Optimisation de forme en mécanique du contact Application à la régulation thermique d’équipements de satellite

Tue, 20 Jan 2026 00:00:00 +0000

2026-2027 Admissions

Thu, 18 Dec 2025 00:00:00 +0000

Application for the M2 SIAM program 2026-2027 is now open.

To apply, please connect to our dedicated website FSA.

Causal inference with Approximate Bayesian Computation for Multivariate Hawkes Processes

Thu, 11 Dec 2025 00:00:00 +0000

Estimation adaptative et optimale par méthodes d’ondelettes

Thu, 11 Dec 2025 00:00:00 +0000

On a discrete framework of hypocoercivity for kinetic equations.

Thu, 11 Dec 2025 00:00:00 +0000

Tensor-Based approaches for non-Intrusive Load Monitoring of French households

Thu, 11 Dec 2025 00:00:00 +0000

Machine Learning for the Control of Collective Dynamics in Particle-Laden Flows

Mon, 08 Dec 2025 00:00:00 +0000

Optimal Sedimenting Particle in Turbulence Benchmark for Bayesian Optimization and Reinforcement Learning

Mon, 08 Dec 2025 00:00:00 +0000

Development of a particle method for the numerical resolution of a drift-diffusion equation and application to streamerse

Tue, 02 Dec 2025 00:00:00 +0000

Application de méthodes d’apprentissage profond pour la résolution des équations de mécanique des fluides dans un local ventilés

Mon, 01 Dec 2025 00:00:00 +0000

Multiscale Modeling of Magnetic Composites Using the MFEM Software

Mon, 01 Dec 2025 00:00:00 +0000

Traitement de l’incomplétude des données pour une meilleure observabilité des réseaux électriques

Tue, 25 Nov 2025 00:00:00 +0000

Apprentissage de fonctions d’énergie via les réseaux de neurones pour l’analyse de stabilité de systèmes faiblement hyperboliques

Wed, 19 Nov 2025 00:00:00 +0000

Meta-modeling applied to the assessment of rockfall risks

Wed, 19 Nov 2025 00:00:00 +0000

Modélisation – prédiction – analyse statistique pour la dégradation d’actifs industriels

Wed, 19 Nov 2025 00:00:00 +0000

Modélisation de dégradation multivariée et durée de vie restante pour la maintenance prévisionnelle

Wed, 19 Nov 2025 00:00:00 +0000

Tests d’adéquation aux processus de dégradation avec ou sans maintenance imparfaite

Wed, 19 Nov 2025 00:00:00 +0000

Détection de cibles multiples et denses par réseau de neurones dans du signal radar

Fri, 14 Nov 2025 00:00:00 +0000

Détection de formes d’onde électromagnétiques par deep learning avec intégration dans GNU Radio

Fri, 14 Nov 2025 00:00:00 +0000

Graph-Based Multimodal Deconvolution of Spatial Transcriptomics Using H&E Images

Mon, 10 Nov 2025 00:00:00 +0000

Validation des méthodes d’identification symbolique pour les systèmes dynamiques

Mon, 10 Nov 2025 00:00:00 +0000

Detection and Characterization of Protoplanets in Stellar Halos

Mon, 03 Nov 2025 00:00:00 +0000

Generative models for functional protein design

Mon, 03 Nov 2025 00:00:00 +0000

On structure, co-evolution and design of pore-forming and microbial peptides Environment

Mon, 03 Nov 2025 00:00:00 +0000

Améliorer la capacité de prévision immédiate de précipitations

Wed, 29 Oct 2025 00:00:00 +0000

Kernel methods for Computed Tomography

Mon, 27 Oct 2025 00:00:00 +0000

Online Simulation-Based Inference for Large-Scale Scientific Models

Mon, 27 Oct 2025 00:00:00 +0000

Quantized Features for Large-Scale Nonparametric Regression

Fri, 24 Oct 2025 00:00:00 +0000

Sur les traces des débris d’avalanches par imagerie SAR satellites multi-bandes

Fri, 24 Oct 2025 00:00:00 +0000

Modélisation et apprentissage pour le suivi des centrales solaires

Thu, 23 Oct 2025 00:00:00 +0000

Modelling soap film curvature in foams

Thu, 23 Oct 2025 00:00:00 +0000

Optimisation convexe pour l’estimation spectrale

Thu, 23 Oct 2025 00:00:00 +0000

Comparison of Distributions and Change-Point Detection in Spatial Extremes

Tue, 21 Oct 2025 00:00:00 +0000

Développement et validation d’une approche de pilotage par extrapolation, et mise en place de cas-tests

Tue, 21 Oct 2025 00:00:00 +0000

Étude et simulation du modèle couplé Saint Venant/Exnerg

Tue, 21 Oct 2025 00:00:00 +0000

Explora(on d’approches causales pour détecter et quan(fier les déterminants de l’observance au traitement par pression posi(ve con(nue dans le syndrome d’apnées du sommeil.

Tue, 21 Oct 2025 00:00:00 +0000

Validation de modèles numériques pour la simulation du contact-frottement pour des études industrielles

Tue, 21 Oct 2025 00:00:00 +0000

Design of experiments involving stereocorrelation for the resistance of nuclear power plant piping

Sat, 18 Oct 2025 00:00:00 +0000

Effets de phase géométrique en PolInSAR : Estimation de déphasage et géométrie Riemannienne

Thu, 16 Oct 2025 00:00:00 +0000

Machine learning to uncover the physics of Earth’s magnetic field generation

Wed, 15 Oct 2025 00:00:00 +0000

Performance Bound Estimation using Score-Based Models

Mon, 13 Oct 2025 00:00:00 +0000

Spatial Processes for Single-Cell Transcriptomics

Mon, 13 Oct 2025 00:00:00 +0000

Conception optimale de microstructures déployables

Thu, 09 Oct 2025 00:00:00 +0000

La cryptographie homomorphe au service du calcul scientifique

Tue, 30 Sep 2025 00:00:00 +0000

Scientific Competitions in AI: Measuring and Understanding Participant Engagement

Tue, 30 Sep 2025 00:00:00 +0000

Analyse d'incertitudes dans une chaîne de calcul en rentrée atmosphérique

Mon, 29 Sep 2025 00:00:00 +0000

Analysis of microfluidics data for the mathematical investigation of growth variability in bacterial population

Thu, 25 Sep 2025 00:00:00 +0000

Domain adaptation for the prediction of adsorption energies

Wed, 17 Sep 2025 00:00:00 +0000

Suivi temporel des régimes thermiques des sols en milieu subarctique

Wed, 17 Sep 2025 00:00:00 +0000

Modélisation par chaînes de Markov couplées de la dynamique conjointe d’occurrences de séismes

Wed, 05 Mar 2025 00:00:00 +0000

Algorithme de découpe 5 axes de pièces planes chanfreinées

Wed, 12 Feb 2025 00:00:00 +0000

A word from the alumni : Enrico Agrippino

Mon, 10 Feb 2025 00:00:00 +0000

My name is Enrico, I joined M2 SIAM as Erasmus student from Politecnico di Torino, in Italy, where I studied Mathematical engineering. I chose to pursue the double degree program with M2 SIAM because I wanted to have an international university experience in France, and SIAM offers a good combination between mathematical theoretical knowledge and hands-on IT projects. I had my internship at Amadeus, in Sophia Antipolis, where I was then hired as a permanent employee and where I am currently working as a Data Analyst. I would highly recommend SIAM Master as it prepared myself in the best way to the working environment, with a strong theoretical knowledge and a good practice of working in teams developed during the university projects During my semester in the M2 SIAM program in Grenoble, I took courses that allowed me to explore various topics in machine learning and discover state-of-the-art work being done in this field. I was able to complete projects on real-world data and work with experts on how to solve various issues using machine learning and data science methods. It was a great experience to collaborate with people from all over the world and from different academic backgrounds.

For these reasons, I strongly recommend the M2SIAM program, especially for international students.

A word from the alumni : Maxime Renard

Mon, 10 Feb 2025 00:00:00 +0000

My name is Maxime Renard, former M2 SIAM student, currently doing a PhD in the Laboratoire Jean Kuntzmann (LJK) on Grenoble Alpes campus dealing with fluid mechanics modelling for biology. It is the same laboratory as the one I did my M2 SIAM internship in, about a related topic. Before attending this M2, I followed a Bachelor Degree in Informatics and Mathematics (“IMA” track in UGA), and the M1 Applied Maths, which could be seen as a dive into how mathematics are used in our lives.

This made me fall in love with applications, ranging from biology to physics, through seismology and climate study, explaining I chose the M2 SIAM to end this Master. I consider this M2 as a great opportunity; thanks to the projects to produce, the research papers to read and the oral presentations to perform, I really got to learn and play with deep topics, touching direct everyday life applications, such as shape optimization, modelling, signal processing, developing efficient simulation code etc. This Master was overall a knowledge widening experience, I wish everyone could attend.

Machine Learning for Cancer Detection: Identifying Residual Tumor DNA from Blood Samples

Tue, 04 Feb 2025 00:00:00 +0000

Probabilistic modelling of building energy performance

Tue, 04 Feb 2025 00:00:00 +0000

A word from the alumni : Mariya Gorpinich

Mon, 20 Jan 2025 00:00:00 +0000

My name is Mariya Gorpinich, I’m currently have a CIFRE PhD contract with Valeo Group and École nationale supérieure d’Arts et Métiers. My area of research is physics-informed machine learning. My end-of-studies internship was also at Valeo. I chose the M2 SIAM master’s degree because of its variety of high-quality courses on machine learning and computer science. Before my master’s studies, I finished my bachelor’s studies at the Moscow Institute of Physics and Technology. I recommend the M2 SIAM master’s degree to anybody interested in the field of machine learning and computer science.

A word from the alumni : Julien Lalanne

Mon, 13 Jan 2025 00:00:00 +0000

My name is Julien Lalanne, and I graduated from the MSIAM Master’s program in 2023, earning a double diploma with ENSIMAG. During my studies,I completed an internship at the NationalInstitute of Informatics in Tokyo, which greatly enriched my experience.For the past year, I have been working at TotalEnergies,focusing on soil parameter prediction for wind farm foundation design. Soon, I will begin a Ph.D.in conditional generative models for 3D high-frequency data.I chose this Master’s program to advance my expertise in Deep Learning, and I highly recommend it to students aiming to excel in data science.

Simulation sociospatiale à base d’agents des exploitations agricoles et du territoire de la moyenne vallée du Litani dans la Bekaa, Liban

Fri, 10 Jan 2025 00:00:00 +0000

Web scraping et analyse de données environnementales avec Python

Tue, 07 Jan 2025 00:00:00 +0000

A word from the alumni : Julien Prando

Mon, 06 Jan 2025 00:00:00 +0000

My name is Julien Prando, and I am currently a PhD student at the Laboratoire Jean Kuntzmann (LJK) at Université Grenoble Alpes in France. My PhD thesis is entitled ”Distributionally Robust Shape Optimization Problem,” which builds upon the master’s internship I completed in the same laboratory, focused on ”Distributionally Robust Optimization.” Prior to my PhD, I completed a Master’s program with a strong foundational background: first with an ”M1 Maths Générales” year, followed by the ”M2 Préparation à l’Agrégation.” This path culminated in obtaining the Agrégation in Mathematics. Following this achievement, I decided to pursue further studies in Applied Mathematics. I chose the MSIAM program because it offers the opportunity to explore a wide variety of fields, allowing me to find my area of interest in order to pursue into a PhD thesis. I highly recommend this master’s program; both the practical and theoretical courses are of outstanding quality.

Émulation par IA d’un modèle de transfert radiatif dans la neige

Fri, 27 Dec 2024 00:00:00 +0000

traitement automatique du langage naturel appliqué à la littérature scientifiques

Fri, 20 Dec 2024 00:00:00 +0000

tSimulation des déplacements et des variations de gravité associés à des sources en contexte volcanique

Fri, 20 Dec 2024 00:00:00 +0000

A word from the alumni : Sophie Christine Stelmach

Mon, 09 Dec 2024 00:00:00 +0000

My name is Sophie and I am from Toronto, Canada. I participated in a double-degree masters program with my host university, McMaster University, and ENSIMAG.

During my semester in the M2 SIAM program in Grenoble, I took courses that allowed me to explore various topics in machine learning and discover state-of-the-art work being done in this field. I was able to complete projects on real-world data and work with experts on how to solve various issues using machine learning and data science methods. It was a great experience to collaborate with people from all over the world and from different academic backgrounds.

For these reasons, I strongly recommend the M2SIAM program, especially for international students.

Equité et performance prédictive du ciblage des campagnes de prévention dans le SNDS : quel apport d’un modèle d’IA prédictif par rapport à des règles métiers ?

Sat, 07 Dec 2024 00:00:00 +0000

Modèles de régression par processus gaussiens : application à la formulation de fluides pour la mobilité.

Sat, 07 Dec 2024 00:00:00 +0000

TAI-Generated Hierarchies for Computational Systems Biology Models

Sat, 07 Dec 2024 00:00:00 +0000

Traitement Automatique d’Images Scannées et Mesures Morphométriques d’Os Longs par Machine Learning et Deep Learning.

Sat, 07 Dec 2024 00:00:00 +0000

Traitement d’images spectrales de microscopie électronique assistée par IA appliqué à la classification de météorites.

Sat, 07 Dec 2024 00:00:00 +0000

Modélisation de l'émergence d'architecture des tissus biologiques

Fri, 06 Dec 2024 00:00:00 +0000

Modélisation aléatoire et inférence statistique pour des processus de dégradation avec maintenance imparfaite

Thu, 28 Nov 2024 00:00:00 +0000

Multi-scale Mechanics of embryo Morphogenesis

Thu, 28 Nov 2024 00:00:00 +0000

Non parametric statistical properties for generative adversarial networks models

Thu, 28 Nov 2024 00:00:00 +0000

Integrative Analysis of Clinical and Multi-Omics Data for Blood Cancer Treatment Responses)

Wed, 27 Nov 2024 00:00:00 +0000

Détection de précurseurs d'incidents à partir des gisements de données de Télésurveillance (Maintenance Prédictive)

Tue, 26 Nov 2024 00:00:00 +0000

Arbitrary-Eulerian-Lagrangian schemes for continuum mechanics on polyhedral grids

Mon, 25 Nov 2024 00:00:00 +0000

Comprendre, mesurer et renforcer la robustesse d’un système en cas de crise humanitaire

Tue, 19 Nov 2024 00:00:00 +0000

On the shape of conceptual structures : geometry of concept lattices and n-lattices

Tue, 19 Nov 2024 00:00:00 +0000

Spatial Processes for Single-Cell Transcriptomics

Tue, 19 Nov 2024 00:00:00 +0000

Application of the Discontinuous Galerkin Method for Solving Advection-Diffusion-Reaction Equations in Electrical Breakdown Modeling

Mon, 18 Nov 2024 00:00:00 +0000

Préparation à l’utilisation de TRISHNA pour la modélisation du pergélisol: dérivation de température de surface de sol arctique sur la base de données satellitaires

Mon, 18 Nov 2024 00:00:00 +0000

Metabolic fitness landscapes in fluctuating environments

Thu, 14 Nov 2024 00:00:00 +0000

Using phages to control the gut microbiome: a mathematical modeling approach

Thu, 14 Nov 2024 00:00:00 +0000

Internship on lubrication forces between objects near contact in a fluid

Tue, 12 Nov 2024 00:00:00 +0000

Dynamics of a gas/liquid interface in an open cavity flow

Mon, 11 Nov 2024 00:00:00 +0000

Transformées Intégrales Haute-Performance pour l’Astrophysique de Demain

Wed, 06 Nov 2024 00:00:00 +0000

Modeling and evolution of 3D underwater environments: generation and simulation of phenomenological scenarios

Tue, 05 Nov 2024 00:00:00 +0000

Orthotropic maps for mesh generation

Tue, 05 Nov 2024 00:00:00 +0000

Développement de modèle de détection d’intrusion réseau

Mon, 04 Nov 2024 00:00:00 +0000

Pseudo-labelling in Semi-Supervised Learning: from theory to algorithms

Mon, 04 Nov 2024 00:00:00 +0000

Scalable unsupervised subtle anomaly detection from longitudinal MR imaging data: Application to Parkinson’s disease

Mon, 04 Nov 2024 00:00:00 +0000

Towards expressive and tractable surrogate models for large scale inverse problems

Mon, 04 Nov 2024 00:00:00 +0000

Structure-preserving reduced order models for conservation laws

Tue, 29 Oct 2024 00:00:00 +0000

Topology Optimization of 3D low frequency Electromagnetic devices

Tue, 29 Oct 2024 00:00:00 +0000

Data Scientist

Mon, 21 Oct 2024 00:00:00 +0000

Concentration of the geometry of empirical risks

Fri, 18 Oct 2024 00:00:00 +0000

Macroscopic friction of fibrous assemblies

Tue, 15 Oct 2024 00:00:00 +0000

Numerical modelling of granular avalanches through a forest of deformable pillars

Tue, 15 Oct 2024 00:00:00 +0000

Observations satellitaires et in-situ pour un meilleur suivi de la neige saisonnière et de ses évolutions

Mon, 14 Oct 2024 00:00:00 +0000

Deep learning assimilation of digital phenotyping data for the simulation of a 3D structure-function model of fruit trees

Fri, 11 Oct 2024 00:00:00 +0000

Supervised Data Selection for Large-Scale Nonparametric Regression

Thu, 10 Oct 2024 00:00:00 +0000

Improving Interference Removal Using Deep Learning and Ridges Reassignment via Curve Superresolution

Mon, 07 Oct 2024 00:00:00 +0000

Mathematical modelling and simulation of the emergence of growth variability in bacterial population

Mon, 07 Oct 2024 00:00:00 +0000

Biomechanical Solver for Musculo-Skeletal Rigid Body Motion with Wrapping Surfaces

Fri, 04 Oct 2024 00:00:00 +0000

Data augmentation of medical images for deep learning using personalized biomechanical modeling of the lower limb

Fri, 04 Oct 2024 00:00:00 +0000

Développeur maillage volumique F/H

Fri, 04 Oct 2024 00:00:00 +0000

Portable High-Performance Ocean Simulations

Mon, 30 Sep 2024 00:00:00 +0000

Using Machine Learning to Accelerate PDE solvers

Mon, 30 Sep 2024 00:00:00 +0000

Predicting Adaptation and Maladaptation of Populations to a Changing Climate

Thu, 26 Sep 2024 00:00:00 +0000

Sensorimotor representation learning of manipulable objects

Thu, 26 Sep 2024 00:00:00 +0000

Modélisation numérique de la propagation d’onde dans des réseaux complexes

Tue, 17 Sep 2024 00:00:00 +0000

Dérivation d’un modèle d’écoulement de gaz basé sur le principe d’homogénéisation dans un milieu poreux

Mon, 11 Mar 2024 00:00:00 +0000

Physics-informed Machine Learning for Defect Detection in Medical Microbatteries

Mon, 11 Mar 2024 00:00:00 +0000

Development of the Discontinuous Galerkin Method for Solving Advection-Diffusion-Reaction Equations Used for Modeling Electrical Engineering Problems

Mon, 18 Dec 2023 00:00:00 +0000

POD-NN based PDE model reductions with large dimensional parameter

Mon, 18 Dec 2023 00:00:00 +0000

Modélisation numérique d'écoulements astrophysiques

Sat, 16 Dec 2023 00:00:00 +0000

Un financement de stage de recherche (niveau M2, poursuite en thèse possible) est disponible à l’Inria dans le cadre du projet ERC CIRCE. Le candidat recruté rejoindra l’équipe de “Mécanique des Fluides et Modélisation Numérique” du Laboratoire J.A. Dieudonné, au Département de Mathématiques & Applications de l’Université Côte d’Azur, à Nice (localisation sur le campus du Parc Valrose, voir Liens utiles ci-dessous).

Le projet portera sur la modélisation numérique d’écoulements astrophysiques, et plus particulièrement pour l’étude de la déstabilisation des disques stellaires. Ces disques de poussière et de gaz partiellement ionisé, en rotation Képlerienne autour d’une étoile jeune, sont connus comme linéairement stables (c’est-à-dire stable vis-à-vis de perturbations infinitésimales). Pourtant, les taux d’accrétion surprenants qu’ils présentent suggèrent qu’une source de turbulence, d’origine mal comprise, pourrait être à l’oeuvre dans ces objets et influencer considérablement leur évolution. Le projet visera à identifier, à l’aide de méthodes d’optimisation, des perturbations d’amplitude finie capables de déstabiliser non-linéairement un modèle de disque stellaire.

Des compétences en modélisation, en méthodes numériques pour les EDP et en programmation sont attendues. Des connaissances en calcul variationnel / méthodes adjointes seront un bonus. En fonction de l’intérêt du candidat, des collaborations pourront être développées avec l’équipe EDP du LJAD et l’Observatoire de la Côte d’Azur. Des financements sont également garantis pour favoriser la mobilité du candidat recruté (déplacements en conférences, écoles d’été etc.)

Liens utiles:

le laboratoire: https://math.unice.fr/index.html
le campus Valrose: https://univ-cotedazur.fr/vie-des-campus/visite-des-campus/campus-valrose

Contact: florence.marcotte@inria.fr

3D Computer Vision - Samp

Thu, 14 Dec 2023 00:00:00 +0000

3D Instance Segmentation

Thu, 14 Dec 2023 00:00:00 +0000

Apport des températures de surface satellitaires pour le suivi de la végétation et des cycles thermique et hydrique du sols

Tue, 12 Dec 2023 00:00:00 +0000

Apprentissage pour les données fonctionnelles

Tue, 12 Dec 2023 00:00:00 +0000

Implementation of Frequency Analysis Algorithms for Side-Channel Attacks

Tue, 12 Dec 2023 00:00:00 +0000

Représentation énergétique des systèmes décrits par équations aux dérivées partielles (EDP) – Application aux phénomènes électromagnétiques

Tue, 12 Dec 2023 00:00:00 +0000

Calibration de jeux sérieux

Sun, 03 Dec 2023 00:00:00 +0000

Inference of Twitter interaction graph: Hawkes processes and Stochastic Block Model

Sun, 03 Dec 2023 00:00:00 +0000

Point process models for time dependent extreme data

Sun, 03 Dec 2023 00:00:00 +0000

Optimisation topologique d’un capteur de courant

Wed, 29 Nov 2023 00:00:00 +0000

Data Science et Intelligence Artificielle pour la Recherche de Nouveaux Matériaux Semi-conducteurs pour l'Énergie

Tue, 28 Nov 2023 00:00:00 +0000

Deep Learning Plug&Play Image Reconstruction for Positron Emission Tomography

Tue, 28 Nov 2023 00:00:00 +0000

Développement de modèles d'apprentissage pour le système de détection de la Sonde Atomique Tomographique

Tue, 28 Nov 2023 00:00:00 +0000

Analyse des dynamiques d'un système Zooplancton-Algues-Virus

Fri, 24 Nov 2023 00:00:00 +0000

Measure of extreme inequality in wealth distribution

Tue, 14 Nov 2023 00:00:00 +0000

Geometric Statistics

Sun, 12 Nov 2023 00:00:00 +0000

Analyse et suivi de la qualité de l'air intérieur

Thu, 09 Nov 2023 00:00:00 +0000

Curved folding with inflatables

Thu, 09 Nov 2023 00:00:00 +0000

Numerical optimization of periodic inflatable materials

Thu, 09 Nov 2023 00:00:00 +0000

Solving large sparse linear systems with mixed precision domain decomposition methods

Thu, 09 Nov 2023 00:00:00 +0000

Développement d’un module logiciel pour la conception de systèmes multiphysiques innovants - IRT SystemX

Wed, 08 Nov 2023 00:00:00 +0000

IA quantification des incertitudes NN

Sun, 05 Nov 2023 00:00:00 +0000

Graph Neural Networks applied to radar data for autonomous UAVs

Thu, 26 Oct 2023 00:00:00 +0000

Activity detection using tiny neural networks and accelerometer.

Thu, 26 Oct 2023 00:00:00 +0000

Cell Modeling with Brownian Dynamics

Thu, 26 Oct 2023 00:00:00 +0000

Developing language models for 3D structure of nuclei acids

Thu, 26 Oct 2023 00:00:00 +0000

Gaussian Process Prior Variational Autoencoders for Earth Data Time Series Anlaysis

Thu, 26 Oct 2023 00:00:00 +0000

Graph Neural Networks applied to radar data for autonomous UAVs

Thu, 26 Oct 2023 00:00:00 +0000

Large Language Models

Thu, 26 Oct 2023 00:00:00 +0000

Matching, reconstruc1on and learning 3D shapes using high-order polynomials and rota1on-invariant descriptors

Thu, 26 Oct 2023 00:00:00 +0000

MPI parallelization of the SWEET PDE solver code

Thu, 26 Oct 2023 00:00:00 +0000

Portable High-Performance Ocean Simulations

Thu, 26 Oct 2023 00:00:00 +0000

Scalable Variational auto-encoders with complex distributions Application to Earth observation representation learning

Thu, 26 Oct 2023 00:00:00 +0000

Stability of differential operators on corrected surfaces

Thu, 26 Oct 2023 00:00:00 +0000

Tiny Machine learning for human machine interface gesture using ultrasound data

Thu, 26 Oct 2023 00:00:00 +0000

Transformation d’un modèle Structure fonction de pommier pour l’assimilation de données de phénotypage numérique

Thu, 26 Oct 2023 00:00:00 +0000

Two dimension regression model : a theoretical or a computational study from statistics to machine learning

Thu, 26 Oct 2023 00:00:00 +0000

Using Machine Learning to Accelerate PDE solvers

Thu, 26 Oct 2023 00:00:00 +0000

Voice authentication improvements using accelerometer in True Wireless Stereo devices.

Thu, 26 Oct 2023 00:00:00 +0000

Prediction of instability phenomena in mixed rock and ice massifs and run-out of granular flows linked to the thawing of permafrost in high mountains.

Mon, 23 Oct 2023 00:00:00 +0000

Energetics of Jets in Two-Dimensional Oceanic Turbulence

Fri, 20 Oct 2023 00:00:00 +0000

Modélisation numérique des évolutions morphologiques dans des alliages de polymères.

Fri, 20 Oct 2023 00:00:00 +0000

Continuous testing for the Ripley’s K-function of spatial point processes

Mon, 16 Oct 2023 00:00:00 +0000

Differential privacy for spatial point processes

Mon, 16 Oct 2023 00:00:00 +0000

Domain Adaptation in Machine Learning – application to IoT

Mon, 16 Oct 2023 00:00:00 +0000

PINNs 1 models for optimal control strategies of structured epidemiological models.

Mon, 16 Oct 2023 00:00:00 +0000

Rolling instabilities of elastic ribbons

Mon, 16 Oct 2023 00:00:00 +0000

Structural Design with Elastic Ribbons

Mon, 16 Oct 2023 00:00:00 +0000

Uncertainty estimation of ML models performance – application to IoT

Mon, 16 Oct 2023 00:00:00 +0000

Frictional contact in multibody rigid model: Nonsmooth mechanical method and others approaches in commercial software

Sun, 15 Oct 2023 00:00:00 +0000

Modélisation numérique des évolutions morphologiques dans des alliages de polymèress

Mon, 09 Oct 2023 00:00:00 +0000

Application d’une plateforme incertitude & optimisation afin de mener des études statistiques sur les Accidents Graves

Mon, 02 Oct 2023 00:00:00 +0000

Développement d’un schéma numérique de résolution 1D des équation d’Euler en multiphase

Mon, 02 Oct 2023 00:00:00 +0000

Développement de schémas numériques de résolution des équations d’Euler 1D pour la capture de chocs

Mon, 02 Oct 2023 00:00:00 +0000

Développements et validations du code de calcul accident grave TOLBIAC -ICB

Mon, 02 Oct 2023 00:00:00 +0000

Etude CFD d’ébullition en film

Mon, 02 Oct 2023 00:00:00 +0000

Using the second principle of thermodynamics as a numerical stability criterion in snow models

Mon, 02 Oct 2023 00:00:00 +0000

Validation des modèles multiphasique et termes de transferts d’un code CFD sur les Accidents Graves des réacteurs nucléaires

Mon, 02 Oct 2023 00:00:00 +0000

Brain-based learning networks

Sat, 23 Sep 2023 00:00:00 +0000

Modèles PINNs 1 pour l’élaboration de stratégies de contrôle optimal de modèles épidémiologiques structurés.

Sat, 23 Sep 2023 00:00:00 +0000

Assimilation de données pour la reconstruction du flux de chaleur en rentrée atmosphérique

Sun, 02 Jul 2023 00:00:00 +0000

Apprentissage statistique pour la prévision de la rentrée atmosphérique

Sun, 02 Jul 2023 00:00:00 +0000

Various offers

Fri, 24 Mar 2023 00:00:00 +0000

Amélioration du schéma numérique de simulation d’un système hyperbolique application à une expérience de décantation

Sat, 04 Feb 2023 00:00:00 +0000

Inversion d’images radar pour la reconstruction de champs de vagues

Thu, 02 Feb 2023 00:00:00 +0000

Réseau de neurones bayésien pour l’interprétation incertaine de données géophysiques

Thu, 02 Feb 2023 00:00:00 +0000

Data Engineer

Sun, 29 Jan 2023 00:00:00 +0000

Support for the porting and optimization of a collaborative numerical simulation code on the GPGPU architectures of supercomputers

Sun, 29 Jan 2023 00:00:00 +0000

Machine Learning/Archaeology/Geospatial

Tue, 24 Jan 2023 00:00:00 +0000

Modélisation statistique de pluies annuelles en Afrique de l’Ouest à partir de simulations climatiques

Tue, 24 Jan 2023 00:00:00 +0000

« Biométrie des Objets » - Caractérisation unique d’objets par acquisition d’images contrôlées et apprentissage profond (TI + IA)

Mon, 16 Jan 2023 00:00:00 +0000

Algorithmes d’apprentissage automatique pour l’estimation de l’impact des sécheresses sur les flux de carbone dans les forêts européennes

Mon, 16 Jan 2023 00:00:00 +0000

Stéganalyse et Amélioration d’un code de sécurité par apprentissage profond (TI + IA)

Mon, 16 Jan 2023 00:00:00 +0000

Super-Résolution à partir d’images multiples par apprentissage profond (TI + IA)

Mon, 16 Jan 2023 00:00:00 +0000

Débruitage de séries temporelles pour l’amélioration de la navigation inertielle

Fri, 06 Jan 2023 00:00:00 +0000

Deep diffusion models for anomaly detection

Fri, 06 Jan 2023 00:00:00 +0000

Towards a dynamical core for weather simulations with RHEOLEF

Wed, 04 Jan 2023 00:00:00 +0000

High-performance computing for seismic modeling with the SEM46 development

Sun, 01 Jan 2023 00:00:00 +0000

High-Performance Computing with Ocean Simulations

Sun, 01 Jan 2023 00:00:00 +0000

Étude expérimentale de l’érodabilitédes microplastiques en milieuxvaseux

Wed, 21 Dec 2022 00:00:00 +0000

Flow visualization in 3D porous media

Wed, 21 Dec 2022 00:00:00 +0000

FLUTURA: Fluids, Turbulence, Advection

Wed, 21 Dec 2022 00:00:00 +0000

Sédimentation d'un matériau granulaire dans un fluide : application à la sédimentation de mélanges sable/vase

Wed, 21 Dec 2022 00:00:00 +0000

Crystal structure prediction using geometric deep learning

Mon, 19 Dec 2022 00:00:00 +0000

Developing language models for 3D structure of nuclei acids

Mon, 19 Dec 2022 00:00:00 +0000

Developing language-graph models for evolu5onary spliced graphs

Mon, 19 Dec 2022 00:00:00 +0000

Amélioration du schéma numérique de simulation avec MATLAB d’un système hyperbolique : application à une expérience de décantation en colonne

Mon, 12 Dec 2022 00:00:00 +0000

Couplage d’un modèle ODE de bassin d’aération biologique avec un modèle EDP de décanteur : simulation avec MATLAB

Mon, 12 Dec 2022 00:00:00 +0000

Mise en œuvre d’un modèle de paroi pour la simulation d’écoulements en milieu poreux-fluide

Mon, 12 Dec 2022 00:00:00 +0000

Développement de schémas numériques de résolution 1D des équation d’Euler pour la capture de chocs

Thu, 08 Dec 2022 00:00:00 +0000

A Neural Network Approach for Point Cloud Compression Artifact Removal

Tue, 06 Dec 2022 00:00:00 +0000

Apport des méthodes d’apprentissage profond à la description de données radar

Tue, 06 Dec 2022 00:00:00 +0000

Continuous modeling of the cohesion in granular flows

Tue, 06 Dec 2022 00:00:00 +0000

Finite element method for living tissues

Tue, 06 Dec 2022 00:00:00 +0000

Plan d’expériences, scénarios climatiques et simulation de l’océan

Tue, 06 Dec 2022 00:00:00 +0000

Apprentissage fédéré

Sun, 04 Dec 2022 00:00:00 +0000

Interprétabilité des modèles de machine learning

Sun, 04 Dec 2022 00:00:00 +0000

Perturbation-invariant learning for 3D biological data

Sun, 04 Dec 2022 00:00:00 +0000

Prevision de l’impact des canicules sur le Reseau électrique à partir de modèles fiabilistes

Sun, 04 Dec 2022 00:00:00 +0000

Floor type detection using ultrasound time- of-flight sensors

Mon, 28 Nov 2022 00:00:00 +0000

Investigating model miss specification in simulation based inference

Mon, 28 Nov 2022 00:00:00 +0000

Segmentation optimale du massif ventriculaire cardiaque par tractographie réversible à 2 pas et identification des points stationnaires.

Mon, 28 Nov 2022 00:00:00 +0000

Spectrogram image decomposition separating ridges from their interferences

Mon, 28 Nov 2022 00:00:00 +0000

Video-based dynamic garment representation and synthesis

Mon, 28 Nov 2022 00:00:00 +0000

Video Tracking

Sun, 27 Nov 2022 00:00:00 +0000

Motion compensated reconstruction using deep learning for computational optics

Thu, 24 Nov 2022 00:00:00 +0000

Optimization and learning approach for model identification in quasi-static elastography imaging

Thu, 24 Nov 2022 00:00:00 +0000

Traitement d'image

Thu, 24 Nov 2022 00:00:00 +0000

Denoising speech accelerometer signal with deep learning techniques

Wed, 23 Nov 2022 00:00:00 +0000

Gesture classification using ultrasound data

Wed, 23 Nov 2022 00:00:00 +0000

Sensor simulator

Wed, 23 Nov 2022 00:00:00 +0000

Benchmarking Self-Supervised Learned Models for Human Activity Recognition on smartphones

Fri, 18 Nov 2022 00:00:00 +0000

Continual Learning in IoT – application to Human Activity Recognition (HAR)

Fri, 18 Nov 2022 00:00:00 +0000

Méthode Efficace Pour Le Problème De Transport Optimal Multi-Marginal

Thu, 17 Nov 2022 00:00:00 +0000

Réduction de modèles pour la simulation de mouvements de foules et de matériaux granulaires

Thu, 17 Nov 2022 00:00:00 +0000

Reduction of flow simulations in cracked porous media

Thu, 17 Nov 2022 00:00:00 +0000

Réseaux de tenseurs pour l’équation de Schrödinger dépendante du temps en grande dimension

Thu, 17 Nov 2022 00:00:00 +0000

Comparing approximations in estimating generalized linear mixed models for categorical data

Wed, 16 Nov 2022 00:00:00 +0000

Internships in Artificial Intelligence

Wed, 16 Nov 2022 00:00:00 +0000

Statistical Calculation Of Shape Derivatives

Wed, 16 Nov 2022 00:00:00 +0000

Développement d’algorithmes de Natural Language Processing pour la recherche en oncologie.

Wed, 09 Nov 2022 00:00:00 +0000

Hyperspectral single-pixel image reconstruction using nonnegative matrix factorization and deep learning.

Wed, 09 Nov 2022 00:00:00 +0000

Motion-compensated reconstruction using deep learning for computational optics

Wed, 09 Nov 2022 00:00:00 +0000

Natural Language Processing – Algorithmes de recherche textuelle approximative à grande échelle.

Wed, 09 Nov 2022 00:00:00 +0000

Optimization of a phase change memories simulator using improved numerical methods .

Wed, 09 Nov 2022 00:00:00 +0000

Machine learning methods for the hedging problem in finance.

Tue, 08 Nov 2022 00:00:00 +0000

Apprentissage par renforcement pour le contrôle de la forme de systèmes physiques.

Wed, 02 Nov 2022 00:00:00 +0000

Continuous testing for the Ripley’s K-function of spatial point processes

Sat, 29 Oct 2022 00:00:00 +0000

Uncertainty Quantification for Climate Models : Learning Oceanic Convection from high-resolution simulations

Fri, 28 Oct 2022 00:00:00 +0000

Quantum Information & Dynamics

Thu, 27 Oct 2022 00:00:00 +0000

New teaching unit starting in September 2023 [Quantum Information & Dynamics]({{ < ref “../M2SIAM_UE/GBX9AM88.md” > }} “Quantum Information and Dynamics”)

Various projects for the rendering of stylized panorama maps

Tue, 25 Oct 2022 00:00:00 +0000

Line Insertion In A Conforming Tetrahedral Mesh

Mon, 24 Oct 2022 00:00:00 +0000

Various projects for the rendering of stylized panorama maps

Mon, 24 Oct 2022 00:00:00 +0000

ACtive Multimodal mErging: from psychophysics to computational modeling to robotics Modélisation de données psychophysiques de fusion active de données

Sat, 22 Oct 2022 00:00:00 +0000

Calibration d’un modèle hydrologique à partir de données géophysiques et hydrologiques

Sat, 22 Oct 2022 00:00:00 +0000

Evaluation d’une librairie de matrices H2 pour la modélisation des dispositifs de conversion d’énergie électrique

Sat, 22 Oct 2022 00:00:00 +0000

Accélération de la résolution transitoire dans Altair® Flux

Tue, 18 Oct 2022 00:00:00 +0000

Maillage volumique

Mon, 17 Oct 2022 00:00:00 +0000

Calcul du jacobian d’un modèle dynamique de circuit électrique

Sun, 16 Oct 2022 00:00:00 +0000

Dimensionnement automatique des moteurs électriques

Sun, 16 Oct 2022 00:00:00 +0000

Emergent geometry in simplicial complexes out of equilibrium.

Sun, 16 Oct 2022 00:00:00 +0000

Finite Element Simulations of Streamers for Electrical Engineering Applications

Sun, 16 Oct 2022 00:00:00 +0000

Élaboration d'un projecteur 3D multi-échelles d'images radiologiques médicales

Sat, 08 Oct 2022 00:00:00 +0000

Optimization of a phase change memories simulator using improved numerical methods M/F

Sat, 08 Oct 2022 00:00:00 +0000

Linear-time remote homology search using deep learning

Thu, 06 Oct 2022 00:00:00 +0000

Stochastic generation of weather for estimating hydrometeorological extremes. Potential of a Random Pulse Model for Swiss River Catchments

Thu, 06 Oct 2022 00:00:00 +0000

Développement et évaluation d'un modèle compact de transistor MOS FDSOI généré par un réseau de neurones artificiels

Mon, 03 Oct 2022 00:00:00 +0000

The Parareal algorithm for the Resolution of Low-frequency Electromagnetic Problems

Mon, 03 Oct 2022 00:00:00 +0000

Ingénieur en calcul scientifique

Mon, 26 Sep 2022 00:00:00 +0000

Ingénieur en développement C#/C++ H/F - CAO

Sat, 24 Sep 2022 00:00:00 +0000

Développement et comparaison de bases éléments finis pour une équation de transport sur un maillage hexagonal

Wed, 02 Mar 2022 13:00:00 +0000

Mon, 01 Jan 0001 00:00:00 +0000

Accurate computation of curvature in Level Set method

Mon, 01 Jan 0001 00:00:00 +0000

Adaptative analysis of Large dependent datasets, in particular with Matern-Type autocorrelation

Mon, 01 Jan 0001 00:00:00 +0000

Admission

Mon, 01 Jan 0001 00:00:00 +0000

Advanced numerical methods for PDEs and optimal transport problems

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Instructor

Boris Thibert and Clément Jourdana

Syllabus

The goal of this course is to present and analyze a wide range of numerical methods and algorithms that find applications in various modeling fields. The first part is dedicated to the numerical resolution of partial differential equations (PDEs) and the second one to numerical methods for optimal transport problems.

In the first part, we will start by reminding the principle of the finite difference method (FDM) and the finite element method (FEM). Then, they will be compared to the finite volume method (FVM), a method well suited for the numerical simulation of various conservation laws. Also, more advanced type of finite element methods will be considered (e.g. the mixed finite element method or the discontinuous Galerkin method) in order to solve efficiently a larger range of problems. To test these methods, the numerical resolution of convection-diffusion problems will be discussed, with a potential focus on drift-diffusion models used to describe the electron flow in semiconductor devices.

The second part deals with the optimal transport theory. It is an important field of mathematics that was originally introduced in the 1700’s by the French mathematician and engineer Gaspard Monge. This theory has connections with PDEs, geometry and probability and has been used in many fields such as computer vision, economy, non-imaging optics… In the last 15 years, this problem has been extensively studied from a computational point of view and different efficient algorithms have been proposed. In this part, we present the analysis of several algorithms using the notion of duality, such as Auction’s algorithm, Sinkorn algorithm, Oliker-Prüssner algorithm and a Newton algorithm.

For the first part, it is a clear plus to have attended the 2A courses «Partial differential equations and numerical methods » and « Variational methods applied to modelling ».

Assessment

Evaluation: final exam

Amélioration d’un algorithme d’optimisation du placement de capteurs

Mon, 01 Jan 0001 00:00:00 +0000

Amélioration du schéma numérique de simulation d’un système hyperbolique : application au traitement des eaux usées

Mon, 01 Jan 0001 00:00:00 +0000

An introduction to shape and topology optimization

Mon, 01 Jan 0001 00:00:00 +0000

Credits

3 ECTS, C. 18h

Instructors

Eric Bonnetier and Charles Dapogny

Objectives

In a very broad acceptation, shape and topology optimization is about finding the best domain (which may represent, depending on applications, a mechanical structure, a fluid channel,…) with respect to a given performance criterion (e.g. robustness, weight, etc.), under some constraints (e.g. of a geometric nature). Fostered by its impressive technological and industrial achievements, this discipline has aroused a growing enthusiasm among mathematicians, physicists and engineers since the seventies. Nowadays, problems pertaining to fields so diverse as mechanical engineering, fluid mechanics or biology, to name a few, are currently tackled with optimal design techniques, and constantly raise new, challenging issues.

The purpose of this course is to discuss the main aspects related to the numerical resolution and the practical implementation of shape and topology optimization problems, and to present state-of-the-art elements of response. It focuses as well on the needed theoretical ingredients as on the related numerical considerations. More specifically, the following issues will be addressed:

How to define a `good’ notion of derivative for a ``cost’’ function depending on the domain; How to calculate the shape derivative of a function which depends on the domain via the solution of a Partial Differential Equation posed on it;

How to devise efficient first-order algorithms (e.g. steepest-descent algorithms) based on the notion of shape derivative; How to numerically represent shapes so that it is at the same time convenient to perform Finite Element computations on them, and to deal with their evolution in the course of the optimization process.

Prerequisites

Only a basic knowledge of functional analysis and scientific computing will be assumed: differential calculus, Finite Element method, etc.

Analyse statistique du risque de gel dans le contexte du changement climatique

Mon, 01 Jan 0001 00:00:00 +0000

Analyse topologique de données en mécanique des fluidesd

Mon, 01 Jan 0001 00:00:00 +0000

Analysis and optimization of Schwarz algorithms for ocean - sea ice - atmosphere coupling

Mon, 01 Jan 0001 00:00:00 +0000

Application des graphs neural à la prédiction des retards

Mon, 01 Jan 0001 00:00:00 +0000

Apprentissage de modèles à base de règles pour la prédiction de séries temporelles

Mon, 01 Jan 0001 00:00:00 +0000

Apprentissage de noyaux de fragmentation par réseaux de neurones

Mon, 01 Jan 0001 00:00:00 +0000

Bandit Algorithms for Feature Selection

Mon, 01 Jan 0001 00:00:00 +0000

Bike sharing system analysis, a big data approach

Mon, 01 Jan 0001 00:00:00 +0000

Blind inverse problems in microscopy

Mon, 01 Jan 0001 00:00:00 +0000

CEA - Détection d'anomalie sur les flux vidéo des caméras infrarouge de l'expérience WEST H/F

Mon, 01 Jan 0001 00:00:00 +0000

Colorisation d’images automatique à l’aide de réseaux de neurones et méthodes variationnelles

Mon, 01 Jan 0001 00:00:00 +0000

Computational biology

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Public

M2 MSIAM and 3A MMIS

Instructor

Clovis Galiez et Antoine Frenoy (18h CM + 18h CM)

Description

This interdisciplinary course is designed for students with a computational or mathematical background, providing them with the skills necessary to move into bioinformatics and computational biology. The objective is to provide an introduction to the modeling of biological phenomena, and to present advanced software and mathematical tools for the analysis of sequencing data.Through a realistic bacterial outbreak scenario, the first part of the lecture will deal with omics biology from sequence to protein structure. Through a challenging project, you will need to leverage fundamental computational biology concepts and knowledge (standard tools like blast and standard biological databases) in order to implement your own algorithmic and statistical solution.The second part of the course focuses on evolutionary biology and population biology. The central concepts of the field are presented in a computational form, with emphasis on methods for modeling and simulating the phenomena studied. Through data analyses projects and critical reading of research articles, the focus is put on modern research questions (microbiome, drug discovery, etc) involving large datasets and tools from artificial intelligence.

Requirements

Basic statistics (Poisson distribution), algorithmic (complexity), programming (python required, and R or Matlab).

Evaluation

Session one: Evaluation on project report and source code.Session two: oral examination.

Keywords

population biology, evolution, sequence analysis, algorithms for genomics, protein structures, machine learning, stochastic simulations

Contribute to the future of systems neuroscience : develop a virtual reality environment combined with brain-wide imaging and behavioral analysis

Mon, 01 Jan 0001 00:00:00 +0000

Control of parameters in the presence of uncertainties

Mon, 01 Jan 0001 00:00:00 +0000

Cryptographie quantique & Cryptographie post-quantique

Mon, 01 Jan 0001 00:00:00 +0000

Data Science Seminars and Challenge

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Public

M2MSIAM (DS) et M2(TSI / SIGMA)

Instructor

Ronaldo Phlypo and Sana Louhichi

Description

This course contains two parts.

Part I concerns Data challenge.

This part consists in a real problem that is given to the students for which data are readily available. The goal is to have teams of five to six students compete in solving (at least partially) the problem.

The work is spread over the Autumn semester and consists of: building a prediction model or a methodology to solve the problem based on a set of training data, blind evaluation of the model or methodology on a test bench (unseen data, withheld from the students), using an appropriate performance measure.

At the end, the teams will present their solution path in a formal presentation and a short report.

Part II concerns Data Science seminars.

This is a cycle of seminars or presentations with a common factor that is the project of the data challenge. A first seminar will settle the context and the problem for that year’s data challenge.

The other seminars will propose different industrial or academic approaches and problems that are (loosely) related to the objective of the data challenge. Presentations have a time slot of one hour and students will have to read up front some ressources to orient their questions about the subject after the seminar.

Course Outline

Prerequisites

basic concepts on applied mathematics, probability, statistics

Evaluation:

written report (10–20 pages) 40% (discusses the problem, details the developed method(s) with a bibliography covering the state-of-the-art and situates the problem or one of the proposed approaches with respect to one of the seminars in 1–2 pages)
oral presentation 20% (discusses the problem, the proposed technical solution, and perspectives)
project utility 20% (covers a utility vote from the customer/company and⋅or a ranking score of the proposed solution)

Data Scientist

Mon, 01 Jan 0001 00:00:00 +0000

Deep correction of satellite vibrations for image and surface acquisition

Mon, 01 Jan 0001 00:00:00 +0000

Deep generative models for estimating the genomic offset of populations under environmental change

Mon, 01 Jan 0001 00:00:00 +0000

Deep language models for structural biology in 3D

Mon, 01 Jan 0001 00:00:00 +0000

Dereverberation based on time-frequency reassignment and stochastic reverberation modeling

Mon, 01 Jan 0001 00:00:00 +0000

Differential Calculus, Wavelets and Applications

Mon, 01 Jan 0001 00:00:00 +0000

Public

M2 MSIAM (DS, MSCI)

Credits

6 ECTS

Instructors

Sylvain Meignen and Kevin Polisano

Description

The course is structured in two parts, treated respectively and independently by Sylvein Meignen and Kévin Polisano. The first part is devoted to differential calculus and its applications in image restoration and edge detection. The second part is dedicated to the construction and practical use of the wavelet transform. Wavelets are basis functions widely used in a large variety of fields: signal and image processing, data compression, smoothing/denoising data, numerical schemes for partial differential equations, scientific visualization, etc. Connections between the two parts will be made on the aspects of denoising, edge detection and graph analysis.

Course outline

Part I: Differential Calculus

Differentiability on normed vector spaces
Image restoration
Edge detection

Part II: Wavelets and Applications

From Fourier to the 1D Continuous Wavelet Transform
Wavelet zoom, a local characterization of functions
The 2D Continuous Wavelet Transform
The 1D and 2D Discrete Wavelet Transform
Linear and nonlinear approximations in wavelet bases
The graph Fourier and wavelets transforms

Assessments

First session

Part I – Differential calculus: a written exam (3h) [N1]
Part II – Wavelets and application: one project and two lab sessions [N2]

Second session

The student will have the choice of retaking only one or both parts:

Part I: a new written exam (3h)
Part II: continuation of the project after feedbacks from the teacher

Details for N1:

The lab sessions are each one graded out of 2,5 points
The project is graded out of 15 points
- Choice of the article (difficulty, length, …): 1 point
- Summary and outline (in line with the targets announced): 2 points
- Report redaction (including statement of the method, novelty of the paper, …): 3 points
- Codes (from scratch or existing librairies): 4 points
- Numerical results (replication results or extended): 3 points
Interpretations of the results: 2 points

Discretization and implementation of differential equations modeling light-matter interactions

Mon, 01 Jan 0001 00:00:00 +0000

Écoulements thermiques dans des géométries complexes

Mon, 01 Jan 0001 00:00:00 +0000

Efficient linear algebra on GPUs for Gröbner bases computations

Mon, 01 Jan 0001 00:00:00 +0000

Etude de maturité du Calcul Quantique

Mon, 01 Jan 0001 00:00:00 +0000

Event and Self Triggered Stabilizing Controllers for Linear Switched Systems

Mon, 01 Jan 0001 00:00:00 +0000

Event classification of ultrasound data using machine learning

Mon, 01 Jan 0001 00:00:00 +0000

Fluid mechanics and granular matter

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Public

M2 MSIAM (DS, MSCI)

Instructors

Pierre Saramito and Didier Bresch (18h PS + 18h DB)

Description

Course Outline

The first part of the lecture introduce to mathematical modeling of fluid mechanics and the numerical resolution of the associated equations. Equations are classified by three main families of models:

environmental problems: yield stress fluids (Bingham type) for granular matter, e.g. snow avalanches, mud or ice flows, erosion, landslides and volcanic lavas.
industrial problems: viscoelastic fluids (Oldroyd type) for plastic material processes, and metallic alloy.
biological problems: elastoviscoplastic fluids, for blood flows, liquid foam flows, and for food processing (mayonnaise, ketchup, etc).

Equations and models are presented in a continuum setting, and then approximated in time and space. Then, the efficient numerical resolution is addressed with some examples of practical applications.

The second part of the lecture propose a deeper analysis of granular models. The mathematical study of these complex matter is an important numerical and physical challenge. We will show how it requires a general view related to nonlinear PDEs. The objective of this course will be two-fold:

Show how the compressibility and the viscoplasticity of the phenomenon can play an important role
Discuss congestion phenomena in granular media (maximum packing) that can be compared mathematically to floating structure phenomena in the presence of a free boundary.

Students who complete the course will have demonstrated the ability to do the following:

formulate and solve a large number of nonlinear physical and mechanical problems.
demonstrate a familiarity with fluid mechanics and complex materials
synthesize and implement efficient algorithms for various applications of industrial type.

The main idea of this lecture is to motivate by examples interdisciplinary collaborations needed to deal with complex situations.

Prerequisites

Applied mathematics and scientific computation.

Keywords

partial differential equation ; finite element method ; fluid mechanics.

From Basic Machine Learning models to Advanced Kernel Learning

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Instructor

Julien Mairal

Syllabus

Statistical learning is about the construction and study of systems that can automatically learn from data. With the emergence of massive datasets commonly encountered today, the need for powerful machine learning is of acute importance. Examples of successful applications include effective web search, anti-spam software, computer vision, robotics, practical speech recognition, and a deeper understanding of the human genome. This course gives an introduction to this exciting field. In the first part, we will introduce basic techniques such as logistic regression, multilayer perceptrons, nearest neighbor approaches, both from a theoretical and methodological point of views. In the second part, we will focus on more advanced techniques such as kernel methods, which is a versatile tool to represent data, in combination with (un)supervised learning techniques that are agnostic to the type of data that is learned from. The learning techniques that will be covered include regression, classification, clustering and dimension reduction. We will cover both the theoretical underpinnings of kernels, as well as a series of kernels that are important in practical applications. Finally we will touch upon topics of active research, such as large-scale kernel methods and the use of kernel methods to develop theoretical foundations of deep learning models.

Assessment

Evaluation: project (1/2) + final exam (1/2)

Full Stack Dev Junior

Mon, 01 Jan 0001 00:00:00 +0000

Generative, Multimodal AI

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS - 36h

Instructors

Karteek Alahari, Xavier Alameda-Pineda, Ahlame Douzal, Eric Gaussier, Georges Quénot and Didier Schwab

Description

The course is split into two parts. During the first part, a wide range of machine learning algorithms will be discussed. The second part will focus on deep learning, and presentations more applied to the three data modalities and their combinations. The following is a non-exhaustive list of topics discussed:

Computing dot products in high dimension & Page Rank
Matrix completion/factorization (Stochastic Gradient Descent, SVD)
Monte-carlo, MCMC methods: Metropolis-Hastings and Gibbs Sampling
Unsupervised classification: Partitionning, Hierarchical, Kernel and Spectral clustering
Alignment and matching algorithms (local/global, pairwise/multiple), dynamic programming, Hungarian algorithm,…
Introduction to Deep Learning concepts, including CNN, RNN, Metric learning
Attention models: Self-attention, Transformers
Auditory data: Representation, sound source localisation and separation.
Natural language data: Representation, Seq2Seq, Word2Vec, Machine Translation, Pre-training strategies, Benchmarks and evaluation
Visual data: image and video representation, recap of traditional features, state-of-the-art neural architectures for feature extraction
Object detection and recognition, action recognition.
Multimodal learning: audio-visual data representation, multimedia retrieval.
Generative Adversarial Networks: Image-image translation, conditional generation

Assessment

Final exam

Geometric learning for drug design

Mon, 01 Jan 0001 00:00:00 +0000

Geophysical imaging

Mon, 01 Jan 0001 00:00:00 +0000

Crédits

3 ECTS

Public

M2 MSIAM MSCI

Instructor

Ludovic Métivier

Description

In the current context of energy transition and fight against global warming, a precise knowledge of the crust, down to several km depth, has become a critical issue. The crust is the place where are to be found ore resources needed to build electric batteries (rare earth elements) as well as concrete resources for offshore and onshore wind turbines foundations. The crust is also the only place presenting sufficient volumes to store CO2 and H2 in a flexible way. CO2 storage will be a crucial component among industrial solutions to fight against global warming and reach neutral carbon emissions in the next decades.

To these ends, high resolution quantitative estimates of the mechanical parameters of the crust is essential. To perform such estimation, one has to rely on the interpretation of the mechanical waves which travel in the crust. The inference of the mechanical properties of the subsurface from local recording of the mechanical waves at the surface is a mathematical inverse problem. The aim of this course is to provide the mathematical background and the required theoretical tools to introduce high resolution seismic imaging methods to the students, complemented with practical numerical work on schematic examples.

The first main part of the course will be devoted to the theoretical and practical aspects of wave propagation in heterogeneous media. Beginning by some general consideration on hyperbolic partial differential equations, we will see how the elastodynamics equations, representing the propagation of mechanical waves in the subsurface, belong to this category of equations. We will show in particular an energy conservation result based on the symmetry of the underlying hyperbolic system. We will then discuss how to design absorbing boundary conditions for wave propagation problems, to mimic media of infinite extension. This will lead us to the question of numerical approximation to the solution of wave equations in heterogeneous media. We will discuss in details finite-difference schemes, and practical work will be dedicated to the implementation of a finite-difference scheme for the 1D and 2D acoustic equations, and potentially 2D elastic equations.

The second main part of the course will be devoted to the theoretical and practical aspects of seismic imaging using full waveform inversion. We will show how this method is formulated as a nonlinear inverse problem, controlled by partial differential equations representing wave propagation in heterogeneous media. We will discuss how this problem can be solved by local optimization strategies, and review such strategies, from 1st order gradient method to more evolved 2nd order Newton or quasi-Newton methods. The computation of the gradient of the misfit function through the adjoint state method, following optimal control theory, will be extensively presented, as well as its physical interpretation. This theoretical work will be supported by numerical experiments based on the finite-difference wave propagation code developed in the first part of the course. We will then discuss how full waveform inversion is applied in practice, supported by various field data applications examples. This will lead us to discuss current limitations of the method related to its ill-posedness and the lack of regularity of the solution, and give an overview of methodological work currently performed to mitigate these limitations.

Course outline

2 introductory session

main introduction on seismic imaging (to do what?)
main concepts related to general inverse problems

5 modeling sessions

theoretical considerations on hyperbolic systems
how to derive the elastodynamics equations from Newton and Hooke’s law
elastodynamics equations = symmetrizable hyperbolic system, energy conservation
absorbing boundary conditions - numerical approximation to the solution of wave propagation in heterogeneous media (finite-difference, finite element) - practical work : implement 1D and 2D acoustic, + 2D elastic if time allows

5 inverse problem sessions

imaging the crust= nonlinear inverse problem controlled by an hyperbolic PDE
local optimization method
gradient computation through the adjoint state strategy
physical interpretation of the gradient and Hessian operators - implementation of the gradient computation based on the modeling code designed in the first part
full waveform inversion in practice: hierarchical schemes
review of applications - review of current methodological developments

Prerequisite

linear algebra, Euclidean space, basics on functional analysis, basics on wave equation, basics on Fourier analysis and signal processing

Keywords

wave propagation, hyperbolic systems, finite-differences, inverse problems, numerical optimization, imaging

GPU Computing

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS, Lectures 18h, Labs 18h

Instructor

Christophe Picard

Syllabus

In this course, we will introduce parallel programming paradigms to the students in the context of applied mathematics. The students will learn to identify the parallel pattern in numerical algorithm. The key components that the course will focus on are : efficiency, scalability, parallel pattern, comparison of parallel algorithms, operational intensity and emerging programming paradigm. Trough different lab assignments, the students will apply the concepts of efficient parallel programming using Graphic Processing Unit. In the final project, the students will have the possibility to parallelize one of their own numerical application developed in a previous course.

Introduction to parallelism
Introduction to general context of parallelism
Models of parallel programming
Description of various model of parallelism
Paradigm of parallelism
Templates of parallelism
Parallel architectures
Programming tools: Cuda

Prerequisite

C or C++, Compiling, Data structures, Architecture, Concurrency

Assessment

Project

Handling uncertainties in (large-scale) numerical models

Mon, 01 Jan 0001 00:00:00 +0000

Public

M2 MSIAM (DS, MSCI)

Credits

6 ECTS

Instructors

Elise Arnaud, Eric Blayo, Arthur Vidard, Olivier Zahm

Description

Numerical simulation is ubiquitous in today’s world. Initially confined to well-mastered physical problems, it has spread to all fields (oceanography, biology, ecology, etc.), the aim being to make forecasts of the systems under study. This has been possible thanks to the combination of numerical models and access to a considerable amount of data. However, there are many sources of uncertainty in these modelling systems. They can come from poorly known processes, approximations in the model equations and/or in their discretization, partial and uncertain data, … The objective of this course is to explore in depth the mathematical methods that have allowed these two worlds to meet. Firstly, we will focus on sensitivity analysis approaches that allow us to study the behavior of the system and its response to perturbations. In particular, this permits to study the way in which uncertainties are propagated. Next, we will look at data assimilation methods that aim at reducing said uncertainties by combining numerical models and observation data. Finally, the notions of model reduction will be discussed, which allow the implementation of the previous methods on high dimensional problems.

This course is intended for DS and MSCI students and will start with a differentiated refresher course on the necessary basic mathematical notions.

Course outline

General introduction and reminder of the basic concepts
Sensitivity analysis
- Local sensitivity analysis
- Global sensitivity analysis
Data assimilation
- Variational methods
- Stochastic methods
Model reduction
- Gaussian processes
- Polynomial Chaos

Prerequisites

Keywords

numerical model, uncertainty quantification, sensitivity analysis, data assimilation, inverse problem, meta modelisation, dimension reduction

High-performance computing for seismic modeling with the SEM46 development

Mon, 01 Jan 0001 00:00:00 +0000

High-Performance Computing with Ocean Simulations

Mon, 01 Jan 0001 00:00:00 +0000

Hybrid particle methods for the continuum simulation of granular material

Mon, 01 Jan 0001 00:00:00 +0000

Image localisation of shapes and construction of a dynamical data base

Mon, 01 Jan 0001 00:00:00 +0000

Implementing Multi-Armed Bandits in the DBMS

Mon, 01 Jan 0001 00:00:00 +0000

Ingénieur en biostatistique / bioinformatique H/F

Mon, 01 Jan 0001 00:00:00 +0000

Ingénieur.e informaticien.ne en modélisation des plantes et des cultures

Mon, 01 Jan 0001 00:00:00 +0000

Ingénieur.e informaticien.ne en modélisation des plantes et des cultures

Mon, 01 Jan 0001 00:00:00 +0000

Learning, Probabilities and Causality

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Track

M2 MSIAM (DS)

Instructors

Xavier Alameda-Pineda, Karim Assaad, Emilie Devijver, Eric Gaussier, Thomas Hueber

Objectives

The main aim of this course is to provide the principles and tools to understand and master learning models based on probabilities and causality.

Description

Causality is at the core of our vision of the world and of the way we reason. It has long been recognized as an important concept and was already mentioned in the ancient Hindu scriptures: “Cause is the effect concealed, effect is the cause revealed”. Even Democritus famously proclaimed that he would rather discover a causal relation than be the king of presumably the wealthiest empire of his time. Nowadays, causality is seen as an ideal way to explain observed phenomena and to provide tools to reason on possible outcomes of interventions and what-if experiments, which are central to counterfactual reasoning, as ‘‘what if this patient had been given this particular treatment?’’

Course Outline

Probabilistic Learning

In this part of the course we will study various probabilistic models assuming that the causality relationships between random variables are given. We will focus on unsupervised probabilistic models, from classical Gaussian mixtures to more recent variational techniques including diffusion models. A non-exhaustive list of models discussed in class is:

Gaussian mixture models
Hidden Markov models
Probabilistic principal component analysis
Linear dynamical systems (i.e. Kalman filter)
Variational autoencoders, and their dynamical counterpart.
Normalising flows and diffusion models.

Causal Learning

How to represent causal relations through structural causal graphs?
How to infer causal relations from purely observational data, from purely interventional data and from a mixture of them?
How to exploit and reason upon causal knowledge? In particular, can one quantify the relation between a cause and its effect? Can one compute the effect of an intervention? Can one use causal knowledge for counterfactual reasoning or mediation analysis? Theoretical and practical work The course will be divided into lectures and practical sessions aiming to better understand the different notions introduced. The concepts behind causality are not too difficult to grasp but nevertheless differ from traditional probability concepts.

Prerequisites

Probability and statistics background.

Keywords

Objective

Description

Selected references

Pattern Recognition and Machine Learning, by C. Bishop, 2005. [link].

An introduction to variational autoencoders, by D. P. Kingma and M. Welling [link].

The matrix cook book.

Dynamical Variational Autoencoders: A Comprehensive Review, by Laurent Girin et. al. 2021

The Book of Why: The New Science of Cause and Effect, by Pearl and Mackenzie, 2018

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, by Pearl, 1988

Causation, Prediction, and Search, by Spirtes, Glamour and Scheines, 2000

Elements of Causal Inference: Foundations and Learning Algorithms, by Peters, Janzing and Scholkopf, 2017

Causality: Models, Reasoning and Inference, by Pearl, 2009

Living and studying in Grenoble

Mon, 01 Jan 0001 00:00:00 +0000

ISSO International Student Office

ISSO, the international office for students has a very complete website dealing with:

required administrative procedures,
health and social security,
lodging, etc.

ISSO’s office is located on campus and incoming students are highly encouraged to visit them upon arrival (map can be found here).

Quick access

International students in Grenoble

This organisation proposes activities, mentoring, meetings, see their website for more information.

Why choose Grenoble ?

Grenoble is a wonderful city to live in, with a very large and dynamic student population, and a lot to discover.

For a good presentation of the life as a student in Grenoble: take a look at this short film

Presentation of Grenoble University and Technology Institute:

Université Grenoble Alpes (UGA)
Grenoble Institute of Technology (INP){{ :grenoble-by-night.jpg?300|}}

What to do in Grenoble:

[[http://caesugrando.ujf-grenoble.fr/textes/textes.php|{{:skiderando.jpg?300 |}}]]

M2 SIAM

Mon, 01 Jan 0001 00:00:00 +0000

M2 SIAM

Mon, 01 Jan 0001 00:00:00 +0000

Machine Learning analysis of X-ray spectroscopy images

Mon, 01 Jan 0001 00:00:00 +0000

Machine Learning for Physics

Mon, 01 Jan 0001 00:00:00 +0000

Credits

3 ECTS, 18h

Instructors

Vincent Acary

Objectives

Physics-Informed Neural Networks (PINNs): leveraging neural networks for the resolution of physical problems by directly embedding the governing partial differential equations (PDEs) into the loss function, enabling learning constrained by the underlying physics ;
Data-driven models grounded in operator theory: investigation of DeepONet and Neural Operators (FNO, CNO…) as universal approximators of operators between functional spaces, offering discretization-independent generalization capabilities ;
Convexity-preserving networks, convex optimization layers, and equilibrium networks: examination of advanced techniques such as Input Convex Neural Networks (ICNNs), OptNet layers, and implicit fixed-point networks, with applications to structured modeling and differentiable optimization ;
Critical comparison between physics-informed methods and classical solvers: systematic benchmarking of machine learning approaches against established numerical methods — finite elements (FEM), finite differences (FDM), finite volumes (FVM) — across dimensions of accuracy, computational complexity, scalability, and physical interpretability.

Prerequisites

Statistics (Master 1 level), Probability (Master 1 level)

Matching, reconstruction and learning 3D shapes using high-order polynomials and rota1on-invariant descriptors

Mon, 01 Jan 0001 00:00:00 +0000

Mathematical Foundations of Machine Learning

Mon, 01 Jan 0001 00:00:00 +0000

Program

The program is composed of two parts of offline learning and online learning presented below.

Part I: Offline learning (taught by Massih-Reza Amini)

Supervised Learning

This part gives an overview of foundations of supervised learning. We will see that learning is an inductive process where a general rule is to be found from a finite set of labeled observations by minimizing the empirical risk of the rule over that set. The study of consistency gives conditions that, in the limit of infinite sample sizes, the minimizer of the empirical risk will lead to a value of the risk that is as good as the best attainable risk. The direct minimization of the empirical risk is not tractable as the latter is not derivative, hence learning algorithms find the parameters of the learning rule by minimizing a convex upper-bound (or surrogate) of the empirical risk. We present, classical strategies for unconstrained convex optimization: gradient descente, Quasi-Newton approach, and conjugate gradient descente. We present classical learning algorithms for binary classification: the perceptron, logistic regression and boosting by linking the development of these models to the Empirical Risk Minimization framework as well as the Multi-class classification paradigm. Particularly, we present Multi-Layer Perceptron as well as the back-propagation algorithm that is in use in deep learning.

Unsupervised and semi-supervised learning

In this part, we will present generative models for clustering as well as two powerful tools for parameter estimation namely Expectation-Maximization (EM) and Classification Expectation-Maximization (CEM) algorithms. In the context of Big Data, labeling observations for learning is a tedious task. Semiu-supervised paradigm aims at learining with few labeled and a huge amount of unlabeled data. In this part we review the three families of techniques proposed in semi-supervised learning, that is Graphical, Generative and Discriminant models.

Part II: Online learning (taught by Pierre Gaillard and Nicolas Gast)

Adversarial bandits and online learning

This part present different key paradigms that address sequential decision-making under uncertainty. Online prediction with expert advice which focuses on leveraging the wisdom of multiple experts to make predictions, where the goal is to perform nearly as well as the best expert in hindsight. This approach is crucial in scenarios where the true underlying model is unknown, and the learner must adapt to the advice of various experts over time. Online convex optimization extends this concept to more general settings, allowing for the optimization of convex functions in an online manner. Here, the learner makes decisions in a convex set and receives feedback in the form of convex loss functions, aiming to minimize regret over time. And finally, Adversarial bandits introduce a more challenging setting where the learner must explore and exploit in an environment where the rewards are chosen by an adversary. Unlike stochastic bandits, adversarial bandits do not assume any probabilistic structure in the reward generation process, making the learning task significantly more complex. The learner must employ strategies that balance exploration and exploitation effectively, even in the face of potentially malicious reward assignments. These three paradigms collectively contribute to the robustness and versatility of online learning algorithms, enabling them to tackle a wide range of real-world problems characterized by uncertainty and adversity.

Reinforcement learning

This part presents Reinforcement Learning (RL) which is a type of machine learning where an agent learns to make decisions by interacting with an environment, often modeled as a Markov Decision Process (MDP), which provides a mathematical framework describing how the agent’s actions affect the environment’s states and rewards. Classical RL algorithms, such as Q-Learning and SARSA, focus on estimating value functions or direct policy optimization to maximize cumulative rewards. These algorithms have laid the foundation for understanding and solving sequential decision-making problems. Modern RL has seen significant advancements with the integration of deep learning, giving rise to Deep Reinforcement Learning (Deep RL) algorithms like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), which can handle high-dimensional state and action spaces. Additionally, techniques like Monte Carlo Tree Search (MCTS), popularized by AlphaGo, have further enhanced RL by enabling more sophisticated planning and decision-making. These modern approaches have expanded the applicability of RL to complex real-world problems, including game playing, robotics, and resource management, where traditional methods may struggle.

Materials

Slides, homework and passed exams of Part I

In brief

Period : Semester 9
Credits : 6 ECTS
Number of hours : 36h

Pedagogical team

References

[1] Massih-Reza Amini - Machine Learning, de la théorie à la pratique, Eyrolles (2nd edition), 2020.

[2] Christopher Bishop - Neural Networks for Pattern Recognition, Oxford University Press, 1995.

[3] Richard Duda, Peter Hart & David Strok - Pattern Classification, John Wiley & Sons, 1997.

[4] John Shawe-Taylor & Nello Cristianini - Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.

[5] Colin McDiarmid - On the method of bounded differences, Surveys in Combinatorics, 141:148-188, 1989.

[6] Mehryar Mohri, Afshin Rostamzadeh & Ameet Talwalker - Foundations of Machine Learning, MIT Press, 2012.

[7] Bernhard Schölkopf & Alexander J. Smola - Learning with Kernels, MIT Press, 2002.

[8] Vladimir Kolchinskii - Rademacher penalties and structural risk minimization, IEEE Transactions on Information Theory, 47(5):1902–1914, 2001.

Mathematical optimization.

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS, C. 36h

Instructor

Anatoli Iouditski

Syllabus

This course deals with

Topic 1: convex analysis
Topic 2: convex programming
Basic notions: vector space, affine space, metric, topology, symmetry groups, linear and affine hulls, interior and closure, boundary, relative interior
Convex sets: definition, invariance properties, polyhedral sets and polytopes, simplices, convex hull, inner and outer description, algebraic properties, separation, supporting hyperplanes, extreme and exposed points, recession cone, Carathéodory number, convex cones, conic hull
Convex functions: level sets, support functions, sub-gradients, quasi-convex functions, self-concordant functions
Duality: dual vector space, conic duality, polar set, Legendre transform
Optimization problems: classification, convex programs, constraints, objective, feasibility, optimality, boundedness, duality
Linear programming: Farkas lemma, alternative, duality, simplex method
Algorithms: 1-dimensional minimization, Ellipsoid method, gradient descent methods, 2nd order methods
Conic programming: barriers, Hessian metric, duality, interior-point methods, universal barriers, homogeneous cones, symmetric cones, semi-definite programming
Relaxations: rank 1 relaxations for quadratically constrained quadratic programs, Nesterovs π/2 theorem, S-lemma, Dines theorem Polynomial optimization: matrix-valued polynomials in one variable, Toeplitz and Hankel matrices, moments, SOS relaxations

Assessment

A two-hours written exam (E1) in December. For those who do not pass there will be another two-hours exam (E2) in session 2 in spring.

MERLE : Multimodal Elective Representation Learning of Evolution of birds

Mon, 01 Jan 0001 00:00:00 +0000

Méthode de Nitsche pour la discrétisation du contact frictionnel dans les milieux géologiques poreux, déformables et faillés/fracturés

Mon, 01 Jan 0001 00:00:00 +0000

Méthodes de calage de paramètres en temps réel.

Mon, 01 Jan 0001 00:00:00 +0000

Model comparison in multivariate hidden semi-Markov models with covariates Application to modelling flowering sequences in apple trees.

Mon, 01 Jan 0001 00:00:00 +0000

Modeling Seminar

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS, Tut. 36h

Instructors

Christophe Picard

Objectives

This lecture proposes modelling problems. The problems can be industrial or academic. Students are faced to an industrial problem or an academic problem (research oriented). They are in charge of this project. An teacher/tutor may guide them to find solutions to the problem. For industrial project, they have to understand the user needs, to analyze and model the problem, to derive specifications, to implement a solution and to develop the communication and the presentation of the proposed solution. More academic projects are linked to the courses. They are constructed such that the students can go deeper into a subject.

This lecture introduces basic communication methods in industry. This part is in french and optional.

Rules: the students have to choose TWO subjects (either academic or industrial). They work in small groups on both projects with tutor (analysis of the problem, bibliography, construction of a solution, numerical simulations, etc.). At the end, they defend their results in front of a jury and provide a short report.

Prerequisites

No specific prerequisites.

Modélisation du biais dans les recrutements : étude de l'influence d'un biais dans les données d'apprentissage de différentes procédures

Mon, 01 Jan 0001 00:00:00 +0000

Modélisation et simulation numérique de l’extraction de l’eau du sol par les plantes. Amélioration de modèle et application

Mon, 01 Jan 0001 00:00:00 +0000

Modelling and mathematical analysis of complex gravity flows

Mon, 01 Jan 0001 00:00:00 +0000

Modelling presence-only multivariate data through mixtures of multivariate inhomogeneous Poisson processes

Mon, 01 Jan 0001 00:00:00 +0000

MSIAM

Mon, 01 Jan 0001 00:00:00 +0000

Natural Language Processing & Information Retrieval

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The automatic processing of languages, whether written or spoken, has always been an essential part of artificial intelligence. This domain has encouraged the emergence of new uses thanks to the arrival in the industrial field of many technologies from research (spell-checkers, speech synthesis, speech recognition, machine translation, …). In this course, we present the most recent advances and challenges for research. We will discuss discourse analysis whether written or spoken, text clarification, automatic speech transcription and automatic translation, in particular recent advances with multimodal large Language models.

Information access and retrieval is now ubiquitous in everyday life through search engines, recommendation systems, or technological and commercial surveillance, in many application domains either general or specific like health for instance. In this course, we will cover Information retrieval basics, information retrieval evaluation, models for information retrieval, medical information retrieval, and deep learning for multimedia indexing and retrieval.

In brief

Period: semester 9
Credits: 6 ECTS
Number of hours: 36h
Apogée: GBX9MO75

Recommended prerequisites

Basic knowledge in linear algebra, differential calculus and probabilities.

Pedagogical team

Responsibles:

Jean-Pierre Chevallet,
Didier Schwab.

Lecturers:

Laurent Besacier,
Jean-Pierre Chevallet,
Marco Dinarelli,
Emmanuelle Esperança-Rodier,
Lorraine Goeuriot,
Philippe Mulhem,
Didier Schwab
Romain Xu-Darme.

Evaluation

Final written exam, 3 hours.

Numerical Mechanics

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS, C. 36h

Instructors

Florence Bertails-Descoubes, Thibaut Métivet, Mélina Skouras, et Jean Jouve

Description

Applied to both special effects in films and virtual prototyping in industry, physical simulation has become a powerful tool for capturing, predicting, and even understanding the behavior of complex mechanical phenomena. The design of realistic, robust, and efficient mechanical simulators requires complementary skills in various fields such as mechanics, numerical analysis, and algorithmics.

The objective of this course is to provide students with both theoretical and practical tools to understand the important concepts behind physical simulation. The fundamentals of solid mechanics and numerical analysis will be presented and complemented by practical exercises on computers. Furthermore, throughout the sessions, students will have the opportunity to build their own simulator, implementing all the formalisms and techniques seen in the course (rigid body dynamics, contact detection, contact and friction response).

Syllabus

Lagrangian mechanics

Rigid bodies

Optimization under bilateral constraints

Optimization under unilateral constraints, introduction to frictionless contact

Frictional contact and advanced digital methods

Prerequisites

Keywords

Evaluation

Presentation

Online EM algorithm for robust clustering: Application to Parkison disease}

Mon, 01 Jan 0001 00:00:00 +0000

Optimisation de Placement Multi-Marchés

Mon, 01 Jan 0001 00:00:00 +0000

Optimisation par technologie quantique

Mon, 01 Jan 0001 00:00:00 +0000

Parameter identification and sensitivity analysis for mechanical systems with frictional contacts

Mon, 01 Jan 0001 00:00:00 +0000

Phase retrieval from the short-time Fourier transform

Mon, 01 Jan 0001 00:00:00 +0000

Prévision de la fin de vie d’un système industriel par des approches hybridant modèles de fiabilité et apprentissage machine

Mon, 01 Jan 0001 00:00:00 +0000

Quantum Information & Dynamics

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS, C. 36h

Instructors

Brigitte Bidegaray, Clément Jourdana, Alain Joye, Cécilia Lancien

Description

The quantum formalism developed a century ago provides a very precise description of nature at small scales which entails several counter intuitive aspects: superposition of states, entanglement, intrinsic randomness of measurement process, to list a few. However, from a mathematical point of view, quantum mechanics does have a definite formulation. This allows to investigate these intriguing features rigorously and to explore these quantum traits in information theory and algorithmics in particular, as well as the challenges they present.

The goal of these lectures is to provide a mathematical description of the quantum formalism in finite dimension and to introduce the mathematical concepts and tools required for the analysis of such quantum systems and their dynamics. On the one hand, we will study the key aspects of quantum information theory. On the other hand, we will describe certain properties of quantum dynamics that need to be taken into account in the implementation of quantum algorithms and that will be applied to emblematic systems. The interaction with an external classical electromagnetic field will also be considered both from a theoretical and a numerical point of view.

Syllabus

Part I (9 hours) A. Joye : Quantum formalism and Functional analysis in finite dimension

Quantum states, observables, quantum measurement process.
Spectral theorem, functional calculus, Klein’s inequality
Entropies, Gibbs state, variational principle.

Part II (9 hours) C. Lancien : Quantum Information

Multipartite quantum systems: tensor product spaces, partial trace
Entanglement characterization and detection: Schmidt decomposition, entanglement witnesses, entanglement criteria
Quantum channels: representations, output entropies
Quantum information: no-cloning theorem, additivity problems, quantum algorithms

Part III (9 hours) A. Joye : Quantum Dynamics of Open Systems

Quantum trajectory and associated Markov process
Discrete and continuous in time Quantum dynamics, decoherence
Quantum master equation, Markovian approximation, Lindblad dynamics

Part IV (9 hours including 3 hours of practical sessions in Python) B. Bidegaray, C. Jourdana : ODE-PDE modeling and numerical analysis in quantum optics

Numerical resolution of the Lindblad equation (conservation of the physical properties, splitting methods).
Maxwell-Bloch model (Maxwell equations, coupling with Bloch equations, Cauchy problem).
Numerical resolution of Maxwell-Bloch equations (finite difference scheme on staggered grids, stability analysis).

Remark A third of the time will be devoted to exercise sessions.

Prerequisites

Linear Algebra, Analysis, ODE theory (master 1 level)

Keywords

Functional Calculus, Quantum States, Quantum Measurement, Entropies, Tensors, Entanglement, Quantum Channels, Quantum Information, Quantum Algorithms, Open Quantum Systems, Markovian Dynamics, Maxwell Equations, Cauchy Problem, Finite Differences, Splitting.

Evaluation

Random Graphs in Machine Learning

Mon, 01 Jan 0001 00:00:00 +0000

Research Engineer in AI for biological microscopy

Mon, 01 Jan 0001 00:00:00 +0000

Research engineer position in numerical methods

Mon, 01 Jan 0001 00:00:00 +0000

Research in Grenoble

Mon, 01 Jan 0001 00:00:00 +0000

Ridges reassignment without phase information

Mon, 01 Jan 0001 00:00:00 +0000

Rolling instabilities of elastic ribbons

Mon, 01 Jan 0001 00:00:00 +0000

Secure Bandit Algorithms for Recommendations

Mon, 01 Jan 0001 00:00:00 +0000

Simulation numérique du comportement thermo-hydraulique du sous-sol autour d'un site de stockage profond de déchets radioactifs

Mon, 01 Jan 0001 00:00:00 +0000

Simulation of the biogeochemical cycle in an urban lake: impact of external forcings on the ecosystem dynamics

Mon, 01 Jan 0001 00:00:00 +0000

Simulation-based methods for networks inference

Mon, 01 Jan 0001 00:00:00 +0000

Statistical learning: from parametric to nonparametric models

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Public

M2 MSIAM (DS)

Instructors

Sana Louhichi and Anatoli Juditsky

Description

This course is related to mathematical and statistical methods which are very used in supervised learning.

It contains two parts.

In the first part, we will focus on parametric modeling. Starting with the classical linear regression, we will describe several families of estimators that work when considering high-dimensional data, where the classical least square estimator does not work. Model selection and model assessment will particularly be described.

In the second part, we shall focus on nonparametric methods. We will present several tools and ingredients to predict the future value of a variable. We shall focus on methods for non parametric regression from independent to correlated training dataset. We shall also study some methods to avoid the overfitting in supervised learning.

This course will be followed by practical sessions with the R software.

Course Outline

Introduction. Penalized linear methods for regression and classification. Non linear methods for regression. Cross Validation.

Prerequisites

basic probability statistical inference, linear model.

Keywords

High-dimension, Lasso, Ridge, Information Criteria, Mallows criterion, Cross validation, Nonparametric trend estimation, Kernel nonparametric models, Smoothing parameter selection, Average squared error, Mean average squared error, Generalized cross validation, Dependent random variables, Martingale difference sequences, Stochastic Volatility, Moment inequalities, Maximal inequalities. Supervised classification.

Stochastic Calculus and Applications to finance

Mon, 01 Jan 0001 00:00:00 +0000

Credits

3 ECTS, 18h

Instructors

Pierre Etoré

Objectives

This MSc course aims at presenting the fundamental concepts of Stochastic Calculus, and the way this concepts have been used in order to build models for applications to finance. Stochastic calculus is a theory that uses Brownian motion and Itô’s integral as basic building blocks, and Itô’s formula as a multipurpose tool, in order to describe and manipulate a rather large variety of continuous time Stochastic processes , called « continuous semimartingales » (Stochastic calculus for processes with jumps is out of the scope of this course). The theory of Stochastic calculus is largely due to the seminal work by K. Itô, that goes back to the 1940s and 1950s. This work has been rediscovered by economists (among them Myron Scholes) in the 1970s, giving rise to the famous Black-Scholes model. Since the late 1980s the link between Stochastic calculus and economics has been more and more formalized, giving rise to the field of « Mathematical Finance ».

This course requires knowledge of probability and integration theory. Some previous knowledge of Stochastic processes is welcomed. No previous knowledge of Brownian motion or Stochastic Calculus is required. The content is planned to be:

Continuous time stochastic processes, Brownian motion (definition and properties)
Continuous time martingales
Itô’s integral
Itô’s formula, Theorem of Lévy, Theorem of Girsanov
Black-Scholes model; notion of pricing and hedging
Pricing and hedging formulas, illustration of the link between Stochastic Differential Equations and Partial Differential Equations inside Black-Scholes type models.

Resources

Text course of the course: https://membres-ljk.imag.fr/Pierre.Etore/fichiers/poly_SCAF.pdf

Bibliography

“Continuous martingales and brownian motion”, D. Revuz, M. Yor
“Brownian motion and stochastic calculus” I.K. Karatzas S.E. Shreve
“Stochastic Calculus for Finance”, S.E. Shreve

Prerequisites

Statistics (Master 1 level), Probability (Master 1 level)

Structural health monitoring of composite structures: Data mining

Mon, 01 Jan 0001 00:00:00 +0000

Structural Optimization

Mon, 01 Jan 0001 00:00:00 +0000

Temporal, spatial and extreme event analysis

Mon, 01 Jan 0001 00:00:00 +0000

Credits

6 ECTS

Public

M2 MSIAM (DS)

Instructors

Julien Chevallier (part I), Jean-François Coeurjolly (II) and Stéphane Girard (part III)

Description

Modelling extreme temperatures, extreme river flows, earthquakes intensities, neuronal activity, map diseases, lightning strikes, forest fires, for example is a risk modelling and assessment task, which is tackled in statistics using point processes and extreme value theory.

On the one hand, point processes are a class of stochastic processes modelling random events in interaction. By event we can think of the time a neuron activates, an earthquake occurs, the time a tweet has been retweeted, etc or the location of a tree in a forest, the impact of a lightning strike, etc. The first two parts provide an introduction to stochastic models and statistical inference which could cover such applications. Main characteristics of such processes, standard models (properties, simulation) and statistical procedures to infer them will be presented.

On the other hand, taking into account extreme events such as heavy rainfalls, floods, extreme temperatures is often crucial in the statistical approach to risk modeling. In this context, the behavior of the distribution tail is then more important than the shape of the central part of the distribution. Extreme-value theory offers a wide range of tools for modeling and estimating the probability of extreme events.

Course Outline

Part I (9 hours) - Temporal point processes

Definition and simulation of one-dimensional point processes (conditional/stochastic intensity);
Likelihood and goodness-of-fit tests (illustration on the Poisson point process);
Hawkes processes (estimation, goodness-of-fit, stationarity, ergodicity).

Part II (12 hours) - Spatial point processes

Definition and characterization of a spatial point process, intensity functions and conditional intensity functions; Poisson point process;
Intensity estimation and summary statistics;
Models for spatial point processes (Cox, determinantal and Gibbs point processes): characterization, simulation, statistical inference and validation.

Part III (15 hours) - Extreme-value analysis

Asymptotic behavior of the largest value of a sample. Extreme-value Distribution (EVD). Maximum domains of attraction (Fréchet, Weibull and Gumbel). Asymptotic behavior of excesses over a threshold. Generalized Pareto Distribution (GPD). Regularly varying functions.
Estimation of the parameters of the EVD and GPD. Hill estimator. Application to the estimation of extreme quantiles. Illustration on simulated and real data.

Prerequisites

Background on statistics and probability (master 1 level)

Keywords

stochastic processes; dependence modelling; simulation and statistical inference; Poisson point process; quantiles; excess process.

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