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

Postdoc offer on the physics of mechanical metamaterials H/F

Job Description

Job Title: Postdoctoral Researcher – Mechanical Metamaterials Role Summary: Drive research on the physics and design of tunable random architectures for mechanical metamaterials, developing numerical and analytical tools to predict mechanical properties and creating innovative brick‑and‑mortar architectures inspired by osteoderms. Expactations: Deliver high‑quality scientific output (publications, conference presentations, and potential patents), provide leadership in interdisciplinary collaboration, and contribute to the advancement of the PEPR DIADEM and ADAM project ecosystem. Key Responsibilities - Design and implement numerical/analytical models to predict elastic moduli, yield strength, and toughness of metamaterials. - Develop and validate computational tools for architecture design and mechanical behavior simulation. - Create novel architectures inspired by osteoderms, balancing lightness, hardness, and toughness. - Lead experimental validation or collaborate with partners to test model predictions. - Prepare manuscripts, conference presentations, and patent applications. - Coordinate with students, researchers, and technicians to integrate multidisciplinary expertise. Required Skills - Strong background in solid and structural mechanics; proficiency in mathematical and numerical methods. - Expertise in non‑linear physics (fracture mechanics, buckling, mechanical instabilities) and architectural materials. - Experience with computational modeling (finite element analysis, optimization). - Programming skills (e.g., Python, MATLAB, C++). - Excellent scientific writing and communication. - Collaborative mindset in interdisciplinary research environments. Required Education & Certifications - PhD in Physics, Mechanical Engineering, or a closely related field. - Recent PhD graduates strongly encouraged; no additional certification required.

Saclay, France

On site

24-12-2025

Company Name

CEA

Job Title

Internship - Develop a 3D multi-modal annotation tool H/F

Job Description

**Job Title:** Intern – 3D Multi‑Modal Annotation Tool Development (H/F) **Role Summary:** Assist in designing and implementing both backend and frontend components of the open‑source annotation platform PIXANO to enable multi‑modal (camera, LiDAR, RADAR) visualization and annotation of 3D scenes for autonomous‑driving perception data. **Expectations:** - Contribute to a functional prototype by the internship end date. - Write clean, maintainable code in Python and JavaScript. - Collaborate with senior engineers to integrate visualization features (BEV, 3D, NeRF/Gaussian splatting). - Produce documentation and demo material for internal review. **Key Responsibilities:** - Develop and extend PIXANO’s frontend for multi‑modal display (camera, LiDAR, radar, bird’s‑eye view). - Implement backend services for data handling, storage, and retrieval (Python, SQL). - Integrate emerging visualization techniques such as Neural Radiance Fields (NeRF) and Gaussian splatting. - Create annotation tools for 2.5D/3D labeling (object detection, segmentation). - Test and debug the tool across different sensor datasets. - Participate in code reviews and agile sprint meetings. **Required Skills:** - Proficiency in Python and JavaScript (frontend frameworks a plus). - Familiarity with databases and SQL queries. - Strong foundation in computer vision and 3D geometry concepts. - Ability to work on both client‑side and server‑side development. - Good problem‑solving skills and attention to detail. - Basic knowledge of version control (Git). **Required Education & Certifications:** - Currently enrolled in a Master’s program (M1/M2) or equivalent engineering school (Bac+5) in Computer Science, Electrical Engineering, Robotics, or related field. - Expected graduation 2025 or later. - No specific certifications required; academic coursework in computer vision, machine learning, or 3D graphics preferred.

Saclay, France

Hybrid

25-12-2025

Company Name

CEA

Job Title

Design of a Reinforcement Learning–Driven Scheduler for Efficient and Frugal Container Orchestration H/F

Job Description

**Job Title** Design of a Reinforcement Learning–Driven Scheduler for Efficient and Frugal Container Orchestration **Role Summary** Design, implement, and evaluate a reinforcement‑learning (RL) scheduler that optimizes container placement, resource allocation, response time, and energy consumption within modern orchestration frameworks such as Kubernetes or Docker Swarm. **Expectations** - Complete a 6‑month internship project from research to prototype. - Deliver a functional RL scheduler integrated with the team’s orchestration platform. - Produce benchmark results comparing the new scheduler to existing strategies. - Present findings in written reports and technical demos. **Key Responsibilities** 1. Study the existing orchestration framework and internal tooling. 2. Conduct a literature review of RL‑based scheduling in cloud and edge environments. 3. Design the scheduler architecture and data flow. 4. Implement feature extraction modules that capture container behavior and inter‑container dependencies. 5. Develop, train, and fine‑tune RL agents using simulation or real deployments. 6. Configure experiments, collect metrics (latency, throughput, energy), and benchmark against baseline schedulers. 7. Document design decisions, performance results, and recommendations for production rollout. **Required Skills** - Strong programming in Python (proficiency in libraries such as TensorFlow, PyTorch, or RL frameworks). - Foundations in reinforcement learning, machine learning, or deep learning. - Understanding of distributed systems, containerization, and orchestration (Kubernetes, Docker Swarm). - Experience with feature engineering and time‑series data. - Ability to design experiments, run performance benchmarks, and analyze quantitative results. - Excellent written and verbal communication for technical reporting. **Required Education & Certifications** - Final‑year Master’s or Engineer’s degree in Computer Science, Artificial Intelligence, or related field (Bac+5). - Coursework or demonstrable experience in machine learning, distributed systems, and/or cloud computing.

Saclay, France

On site

17-01-2026

Company Name

CEA

Job Title

Post-doctorat en deep-learning et soutenabilité H/F

Job Description

**Job Title** Postdoctoral Researcher – Deep Learning & Sustainability **Role Summary** Conduct advanced research on developing robust, explainable deep‑learning models that incorporate causal relationships to optimize urban heating systems under changing climatic conditions. Lead the design, implementation, and evaluation of continuous‑learning foundation models for on‑edge deployment, ensuring adaptability, low catastrophic forgetting, and integration with existing heating network infrastructure. **Expectations** - Deliver peer‑reviewed publications and technical reports. - Advance methodologies for causal inference in deep learning and incremental learning for embedded systems. - Collaborate with interdisciplinary teams to apply research outcomes to real‑world urban heating subnetworks. **Key Responsibilities** - Design and implement causal‑constraint machine‑learning pipelines to enhance model interpretability, failure‑mode detection, and thermal distribution optimization. - Develop incremental learning strategies for foundation models that mitigate catastrophic forgetting while operating near data sources. - Deploy and validate deep‑learning components on GPU‑enabled edge devices for urban heating control, monitoring, and maintenance. - Conduct impact analyses of new technologies on sub‑station efficiency and sustainability metrics. - Mentor junior researchers and contribute to knowledge sharing within the research group. **Required Skills** - Expertise in deep learning (TensorFlow, PyTorch) and machine‑learning theory. - Proficiency in causal inference techniques and their integration into neural‑network training. - Experience with incremental/continual learning algorithms for embedded models. - Strong applied‑mathematics background (probability, statistics, optimization). - Advanced programming in Python, including GPU and TensorFlow usage. - Fluent written and spoken English. - Ability to translate complex research into practical, deployable solutions for industrial systems. **Required Education & Certifications** - PhD (or equivalent) in Computer Science, Machine Learning, Signal Processing, or related field. - No specific certifications required, but demonstrated mastery of cutting‑edge deep‑learning and causal‑modeling techniques through prior research.

Grenoble, France

On site

20-01-2026