Internship - Machine Learning for Material Science

Position description

**Category**:

- Mathematics, information, scientific, software

**Contract**:

- Internship

**Job title**:

- INTERNSHIP - Machine Learning for Material Science - 6 months - Saclay H/F

**Subject**:

- Optimization Schemes and Machine Learning for Structural Design Applications in Additive Manufacturing

**Contract duration (months)**:

- 6

**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**: we aim to improve our understanding of the evolution of complex systems and their components in material science. 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.**Metallic gaskets** are designed to ensure optimal sealing for components exposed to high pressure and temperature, such as the covers of pressurized water reactors. These gaskets consist of three integral parts, including a rigid spring that forms their core structure. Achieving ideal **elastic deformation** in gaskets can be challenging due to plastic deformation during compression, which impacts their performance. Improving springback properties while minimizing plastic deformation requires exploring innovative approaches, supported by studies to **optimize mechanical responses**, and leveraging advanced manufacturing techniques for prototyping. Preceded by a simulation study that provides the geometric **optimum parameters** of the structure for a tailored mechanical response, complex structures are prototyped with **additive manufacturing**. In order to validate the approach, comparison between **simulation** and **experiments** is realized.- The problem can be framed as **optimization**: we wish to find a distribution of physical and geometrical parameters capable of improving our ability to design good experiments efficiently. Different **machine learning** techniques can be, then, deployed to classify and characterize physical structures for their design optimization. The intern will deal with tasks such as:
- contribute to **data exploration and engineering** for the creation of high-quality reference databases,
- perform a **sensitivity analysis** of the input parameters to determine the impact on the final results (e.g. via Sobol indices and Shapley values),
- ** model** the distributions of the parameters (posterior), based on the material response, to improve the experiment design (e.g. through Monte Carlo simulations),
- identify and propagate **uncertainties** and errors in the models, in order to account for experimental reproducibility and trustworthiness of the measurements,
- perform an **automatic classification** of different material structures using machine learning methods for tabular (e.g. XGBoost) and non structured data (e.g. neural networks) from the simulations and the experiments.
- The internship will be a collaboration between the_ Laboratory of Artificial Intelligence and Data Science_ (LIAD) and the _Laboratory of Engineering of Surfaces and Lasers_ (LISL). The intern will have the possibility to work with scientists with different backgrounds, such as engineering, physics, and AI. They will also have the possibility to get familiar with tools developed by different laboratories.**Methods / Means**:

- additive manufacturing, Bayesian optimization, data engineering, AI, machine learning

**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 **statistical methods** and **data analysis** for analytical science. Good knowledge of a **scientific programming language** is mandatory (e.g. Python, R, C++). Previous experience in machine learning methods is appreciated, but unnecessary. A basic understanding of physics (material science) will be considered a plus.Position location

**Site**:

- Saclay

**Job location**:

- France, Ile-de-France, Essonne (91)

**Location**:

- Saclay

**Languages**:

- French (Fluent)
- English (Intermediate)

Requester

**Position start date**:

- 01/01/2025

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 **:2024-34334**Description de l'unité**:

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