Home
Jobs
CEA-List
Design of a Reinforcement Learning–Driven Scheduler for Efficient and Frugal Container Orchestration

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

Hybrid

Palaiseau, France

Internship

04-11-2025

Share this job:

Score my CV

Skills

Communication Creativity Python Docker Kubernetes Resource Allocation Problem-solving Research Machine Learning Programming Organization Artificial Intelligence

Job Specifications

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.

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.

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

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.

Salary and Benefits

Monthly stipend: €1,400 for Master’s-level (BAC+5) internships of at least 4 months (non-scholarship holders). Scholarship holders: please contact us for details.
Accommodation & travel: Up to €230/month for housing and €230 for round-trip travel (for students living outside Île-de-France, with supporting documents).
Transport: 75% reimbursement of the Navigo pass.

Required level: Master's Degree (Bac+5)

Duration: 6 months

Documents to provide: CV + Cover Letter + Rank

Contact : Kods TRABELSI, kods.trabelsi@cea.fr

About the Company

Digital technology at the heart of our research Based in Paris-Saclay and Grenoble, the CEA-List is an institute of the CEA's Technological Research Division. Dedicated to smart digital systems, it contributes to the development of business competitiveness through the technology development and transfer. The expertise and skills developed by CEA-List's 1,000 research engineers and technicians enable our institute to support more than 200 French and foreign companies in their technological developments every year. Our collabo... Know more