Internship

Saclay

Internship Entry-level / Junior

CEA

Le CEA est un acteur majeur de la recherche, au service de l'État, de l'économie et des citoyens. Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies...

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Posted 17 hours ago

General information

Organisation

The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.

Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.

The CEA is established in ten centers spread throughout France

Reference

2024-34335

Description de l'unité

Notre Service dédié au Génie Logiciel pour la Simulation (SGLS) réalise et maintient des plateformes génériques, pérennes et open source dans le but :

- de développer des codes de calcul parallèles en mécanique des fluides à différentes échelles (https://sourceforge.net/projects/trust-platform/)

- d'exploiter les codes de calculs à l'aide d'outils de mise en données, prétraitements et postraitements, standards ou spécifiques;

-de fournir aux physiciens les méthodes et outils leur permettant d'optimiser leurs conceptions et de traiter les incertitudes de leurs études de sureté.

Le Laboratoire d'Intelligence Artificielle et de science des Données (autrement nommé le LIAD) réalise et maintient une plateforme générique, pérenne et open source pour fournir à nos physiciens des méthodes et outils leur permettant d'améliorer leurs modèles, d'optimiser leurs conceptions et de traiter les incertitudes de leurs études : la plateforme Uranie.

Uranie ? Oui, notre plateforme permet dans l'approche VVQI (Validation, Vérification et Quantification d'Incertitude) de créer des plans d'expériences adaptés aux besoins d'une analyse de sensibilité, d'un problème d'optimisation ou de la génération d'une base d'apprentissage ou de test pour un modèle de substitution.

Uranie permet de piloter le lancement des codes ou fonctions de manière séquentielle ou avec différentes approches de parallélisation.

Position description

Contract

Internship

Job title

INTERNSHIP - High Precision Interpretable Machine Learning - 6 months - Saclay H/F

Subject

Interpretability and High Precision Training for Neural Networks

Contract duration (months)

Job description

At the Institute of Applied Sciences and Simulation for Low-Carbon Energies (ISAS) of the CEA, we focus on research and innovation in analytical sciences. As data analysis plays a pivotal role, we are interested in methodological advancements in statistics, mathematics and computer science, for instance, via the development of state-of-the-art AI models, adapted to our needs.

Neural networks sometimes need to compromise between speed and precision. Training of large architectures might last months and generate huge costs for academia and industry. As a consequence, it is sometimes crucial to cut or optimise the duration of training as much as possible for the task at hand. However, this might come at negative impact on robustness, interpretability or, even, precision. For instance, neural networks are often considered as black boxes, needing huge amounts of data (whose detailed properties and impact are often unknown or unexplored), and training can sometimes rely on low precision floating point numbers (potentially, even boolean variables in modern language models) to spare every possible bit of memory. For certain use cases, precision and interpretability are, nevertheless, fundamental components which cannot be sacrificed : applications to medical diagnosis or nuclear energy are just two straightforward examples.

The internship targets the exploration of the state-of-the-art and the development of optimisation techniques for neural networks. The objective is to find possible ways to increase precision and interpretability of deep learning algorithms. In particular, we shall focus on the following tasks:
critical review the state-of-the-art in neural network optimisation to better understand the critical aspects playing a role in neural network precision;
analysis of second order neural network optimisers for reliable and interpretable machine learning in physics;
generalisation of some proposed techniques to enhance precision in neural network predictions.

We shall first test techniques on simplified models (e.g. reproduction of mathematical functions). We shall then consider physical real-world scenarios, such as applications to fluid dynamics in nuclear energy: the intern will apply different optimisation techniques to the deep learning computation of the initial conditions of a heat diffusion process in a solid-liquid interface. The goal will be to achieve an increased precision, granting access to faster computations by traditional solvers. Such techniques will also be useful for other applications beyond the scope of the current project, such as solving geometrical problems or approximating quantum states with neural networks.

The internship will be a collaboration between the DES (Direction of Energies) and the DRF (Direction of Fundamental Research) of CEA. The intern will be hosted by the Laboratory of Artificial Intelligence and Data Science (LIAD) at the DES, in collaboration with the Institute of Theoretical Physics (IPHT).

Methods / Means

optimisation, deep learning, machine learning, AI, physics

Applicant Profile

We look for a passionate student at the end of their studies (e.g. the French M2 level), with a good understanding of machine learning and coding techniques. Good knowledge of any deep learning framework (PyTorch, JAX, Tensorflow) in Python is mandatory, as well as abiding to good object- oriented coding practices. A basic understanding of physics (statistical mechanics) is appreciated and considered a plus, though not necessary.