CEA

About the Company

The CEA is the French Alternative Energies and Atomic Energy Commission ("Commissariat à l'énergie atomique et aux énergies alternatives"). It is a public body established in October 1945 by General de Gaulle. A leader in research, development and innovation, the CEA mission statement has two main objectives: To become the leading technological research organization in Europe and to ensure that the nuclear deterrent remains effective in the future.

The CEA is active in four main areas: low-carbon energies, defense and security, information technologies and health technologies. In each of these fields, the CEA maintains a cross-disciplinary culture of engineers and researchers, building on the synergies between fundamental and technological research.

The civilian programs of the CEA received 49% of their funding from the French government, and 30% from external sources (partner companies and the European Union).
The CEA had a budget of 4,3 billion euros.

The CEA is based in ten research centers in France, each specializing in specific fields. The laboratories are located in the Paris region, the Rhône-Alpes, the Rhône valley, the Provence-Alpes-Côte d'Azur region, Aquitaine, Central France and Burgundy. The CEA benefits from the strong regional identities of these laboratories and the partnerships forged with other research centers, local authorities and universities.

Listed Jobs

Company Name

CEA

Job Title

Stage d'ingénieur en développement logiciel (H/F)

Job Description

**Job Title** Software Engineering Internship – C++ (Engineering Student) **Role Summary** This internship focuses on performance optimization of the EOS scientific computing library, primarily written in C++ with supplementary Bash/Python scripts. The candidate will analyze current architectural and memory‑management bottlenecks, design targeted improvements, and validate performance gains while preserving numerical accuracy and API modularity. **Expectations** - Deliver measurable reductions in execution time (≥20 % on critical code paths). - Produce architecture and memory‑management prototypes that integrate cleanly with existing modules. - Maintain rigorous documentation, unit tests, continuous integration, and CI/CD pipelines. - Communicate progress clearly to the project team and present findings in technical briefings. **Key Responsibilities** - Profile EOS using tools such as gprof, Valgrind, or perf to identify hotspots. - Refactor code architecture by separating physical models from data tables and reducing inter‑module dependencies. - Modernize interfaces to provide simpler, stable integration points for downstream CFD codes. - Reorganize data structures for cache‑friendly access and reduce repeated memory allocations. - Replace expensive dynamic polymorphism with static alternatives where compile‑time decisions are feasible. - Validate numerical accuracy against reference data; ensure backward compatibility of the public API. - Contribute to automated testing, continuous integration, and maintain comprehensive documentation. **Required Skills** - Proficient in C++ (C++11/14/17), data structures, algorithms, and memory‑management concepts. - Experience with profiling, performance tuning, and code optimization techniques. - Familiarity with scientific computing, numerical methods, or thermodynamics is a plus. - Solid grounding in software engineering practices: version control (git), unit testing, CI pipelines, and documentation. - Ability to analyze and improve complex legacy codebases. - Good French language skills (spoken and written) for internal documentation and communication. **Required Education & Certifications** - Current enrollment in an engineering school or Master’s program (M2) – equivalent to a Bac+5. - Background in computer science or applied mathematics, engineering, or related discipline. - No specific certifications required; experience with scientific computing frameworks is advantageous.

Saclay, France

On site

10-11-2025

Company Name

CEA

Job Title

INTERNSHIP - AI for Thermal Hydraulics - 6 months - Saclay H/F

Job Description

**Job Title:** AI Intern for Thermal Hydraulics Prediction **Role Summary:** Six-month internship focused on developing machine learning models to predict Critical Heat Flux (CHF) in nuclear reactor thermal systems, underpinned by uncertainty quantification and AI methodologies for nuclear safety applications. **Expectations:** - Collaborate with international OECD/NEA AI project on nuclear engineering. - Deliver functional AI models for CHF prediction across reactor geometries. - Document and present findings in technical reports and meetings. **Key Responsibilities:** - Conduct literature review on AI methodologies (e.g., world models, latent space alignment). - Analyze experimental CHF datasets for coherence and feature relevance. - Develop and evaluate machine learning/deep learning architectures for CHF prediction. - Implement uncertainty quantification techniques (e.g., conformal prediction). - Interface with thermal-hydraulics experts to align models with physical constraints. **Required Skills:** - Proficiency in Python, deep learning frameworks (PyTorch, JAX, TensorFlow). - Applied statistics/mathematics with AI/neural network expertise. - Experience in uncertainty quantification methodologies. - Data analysis and code management capabilities. **Required Education & Certifications:** - Master’s degree (or equivalent) in Applied Mathematics, Electrical/Mechanical Engineering, or AI. - Academic focus on AI/ML or nuclear engineering principles. - No certifications specified; bilingualism (French/English) preferred.

Saclay, France

On site

10-11-2025

Company Name

CEA

Job Title

Learning to focus: Physics-Informed Deep Learning for Super-Resolved Ultrasonic Phased-Array Imaging H/F

Job Description

Job title: Learning to Focus: Physics‑Informed Deep Learning for Super‑Resolved Ultrasonic Phased‑Array Imaging (M/F) Role Summary: Develop a physics‑informed deep learning framework that extends the Total Focusing Method (TFM) for ultrasonic phased‑array imaging. Blend physical propagation models with modern neural architectures (e.g., transformers) to enable adaptive, interpretable super‑resolution, grating‑lobe suppression, and sub‑wavelength defect detection in complex materials. Expectations: - Complete a 6‑month contracted internship. - Produce deliverables that demonstrate measurable performance gains over traditional TFM in resolution, speed, and generalisation. Key Responsibilities: 1. Design and train adaptive per‑pixel focusing kernels (Reweighted TFM). 2. Analyse and mitigate grating lobes through learned PSFs and pitch‑sensitivity studies. 3. Validate performance on synthetic and experimental datasets, focusing on sub‑wavelength defect imaging. 4. Integrate joint sound‑speed estimation via differentiable beamforming. 5. Develop transformer‑based models for defect classification using multi‑angle scattering data. 6. Document model interpretability and performance metrics for industrial applicability. Required Skills: - Master’s level background in Electrical Engineering, Applied Physics, Computer Science, or related field. - Proficiency in signal and image processing, deep learning frameworks (PyTorch or TensorFlow). - Strong Python programming skills. - Knowledge of ultrasonic or acoustic imaging, inverse problems, or physics‑informed machine learning. - Experience with data‑driven modeling and algorithmic optimisation for real‑time systems. Required Education & Certifications: - Master’s degree in Electrical Engineering, Applied Physics, Computer Science, or related discipline. - No mandatory certifications required.

Saclay, France

On site

12-11-2025

Company Name

CEA

Job Title

Internship (6 months) in Electromagnetics/AI H/F

Job Description

Job title: Internship (6 months) in Electromagnetics/AI H/F Role Summary: A 6‑month research internship focused on improving electromagnetic backscatter modeling for multi‑static sensing and developing large‑scale simulation datasets to train AI/ML classification algorithms. Expectations: - Master’s level student in Electrical Engineering, Physics, or related field. - Strong foundation in electromagnetic theory and full‑wave simulation. - Proficiency in MATLAB and/or Python programming. - Interest or experience in AI/ML for classification tasks. - Ability to design and execute automated simulations, manage data pipelines, and analyze results. - English proficiency at intermediate level. - Motivation for potential PhD follow‑up in the same domain. Key Responsibilities: - Extend and validate an in‑house EM tool for multi‑static sensing of multi‑dielectric objects. - Integrate realistic 3‑D object models (STL/voxel) into the simulation workflow and benchmark against full‑wave simulations. - Develop scripts to run large‑scale batch simulations varying shape, orientation, and frequency. - Generate, process, and document datasets suitable for AI/ML downstream work. - Collaborate with research teams to refine simulation and classification methodologies. - Produce technical reports and present findings. Required Skills: - Electromagnetic fundamentals (wave propagation, scattering, full‑wave methods). - Programming: MATLAB and/or Python (scripting, data handling). - Familiarity with AI/ML concepts (classification, feature extraction). - Analytical mindset for simulation analysis and performance benchmarking. - Good written and verbal communication in English. Required Education & Certifications: - Current Master’s student or equivalent in Electrical Engineering, Electromagnetics, Applied Physics, or related field. - Coursework or experience in EM modeling, computational electromagnetics, and/or AI/ML. - No specific certifications required.

Grenoble, France

On site

21-11-2025