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Mines Saint-Etienne

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PhD in Orchestrating Concurrent AI Services on Constrained Industrial Edge Devices Mines Saint-Étienne in France

Degree Level

PhD

Field of study

Computer Science

Funding

Available

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Country

France

University

Mines Saint-Etienne

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Keywords

Computer Science
Electrical Engineering
Information Technology
Federated Learning

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About this position

PhD opportunity in France on Orchestrating Concurrent AI Services on Constrained Industrial Edge Devices. The project focuses on how multiple AI applications can share limited computing resources on industrial edge hardware such as NVIDIA Jetson, MCUs, and NPU-enabled devices, reducing the need for additional hardware while improving efficiency.

Research directions include AI/ML resource orchestration and scheduling, Edge AI and distributed intelligence, Federated Learning for industrial cybersecurity and intrusion detection, Split Learning and TinyML, energy-aware and multi-objective optimisation, model compression/pruning/quantisation/knowledge distillation, Neural Architecture Search (NAS), IoT–Edge–Cloud / 5G-6G networking, and privacy, robustness, security, and bias in Federated Learning. The project also emphasizes real-world Industry 4.0 deployment and evaluation.

Supervision is listed by Guillaume Muller (Mines Saint-Étienne), Rosario Patane (Telecom SudParis), and thesis director Nadjib Achir (Inria Saclay). The project is expected to involve industrial collaborations with Orange, Siemens, and NVIDIA, plus access to industrial testbeds, datasets, and deployment environments.

Ideal applicants should have a background in Machine Learning / Deep Learning, Python and/or C/C++/Rust, Edge AI / IoT, Federated or Distributed Learning, Networking / SDN / 5G-6G, cybersecurity, or embedded/resource-constrained computing. Experience with Jetson, Raspberry Pi, MCUs, NPUs, or similar platforms is an advantage.

Expected start: November 2026. Location: Lyon area and Évry/Palaiseau, France. To apply, send a CV, personalised motivation letter, academic transcripts, and at least one reference contact to the supervisors by email. Applications should be tailored and show a genuine understanding of the research topic.

Funding details

Available

What's required

Motivated candidate with a background in Machine Learning or Deep Learning, Python and/or C/C++/Rust, Edge AI or IoT, Federated or Distributed Learning, Networking or SDN or 5G-6G, cybersecurity, or embedded/resource-constrained computing. Experience with Jetson, Raspberry Pi, MCUs, NPUs, or similar edge platforms is an advantage. Applicants should provide a personalised motivation letter and demonstrate a genuine understanding of the proposed research topic.

How to apply

Send a CV, personalised motivation letter, academic transcripts, and at least one reference contact by email to the listed supervisors. Tailor the application to the research topic and avoid generic submissions.

More information can be found here

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