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
2025-38129
Description de l'unité
The French Atomic Energy and Alternative Energies Commission (CEA) is a major player in research, development and innovation. This technological research organization is active in three main areas: energy, information and health technologies, and defense. Recognized as an expert in its fields, CEA is fully integrated into the European research area and is expanding its presence internationally. The Laboratory for Systems and Technology Integration (LIST), located in the southern Île-de-France region (Saclay), has the mission of contributing to technology transfer and promoting innovation in the field of parallel computing systems.
The Digital Systems and Integrated Circuits Department (DSCIN) is a multidisciplinary research team focused, among other topics, on developing solutions for smart orchestration and frugal computing in distributed systems, with a particular emphasis on reinforcement learning-guided approaches.
Position description
Category
Engineering science
Contract
Internship
Job title
Design of a Reinforcement Learning–Driven Scheduler for Efficient and Frugal Container Orchestration H/F
Subject
Context: Modern distributed systems (such as cloud and edge computing platforms) rely on orchestration frameworks like Kubernetes or Docker Swarm to manage the deployment and execution of applications. A key challenge in these environments is how to schedule containers efficiently, deciding which node should run each task, while balancing performance, energy efficiency, and resource usage.
Contract duration (months)
6 months
Job description
Objective: The goal of this internship is to design and evaluate a new intelligent scheduling strategy using reinforcement learning (RL). The idea is to enable the system to learn how to make smarter scheduling decisions over time, optimizing
- container placement and sizing,
- dynamic resource allocation,
- response time and energy consumption
- and even inter-container dependencies such as shared data or communication patterns.
Your missions: During this internship, you will:
- Explore and understand the orchestration framework developed within the team.
- Conduct a state-of-the-art study on RL-based scheduling in cloud and distributed environments.
- Design, implement, and train a new RL-based scheduler.
- Develop a feature extraction module to characterize container behavior and guide the RL agent’s decisions.
- Evaluate your approach through experiments and benchmark comparisons
Applicant Profile
Profile sought
We are looking for a motivated student in the final year of a Master’s or Engineering program in Computer Science, Artificial Intelligence, or a related field, with:
- Good programming skills (Python preferred).
- Interest in machine learning and distributed systems.
- Curiosity, creativity, and strong problem-solving abilities.
Position location
Site
Saclay
Job location
France, Ile-de-France
Location
Palaiseau
Candidate criteria
Prepared diploma
Bac+5 - Diplôme École d'ingénieurs
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