I’m happy to share that we are recruiting a fully funded PhD student at FEMTO-ST Institute and SUPMICROTECH, co-supervised by Karim Cherifi and Jean-Julien Aucouturier
📍 Location: Besançon, France
📅 Start date: September 2026 (flexible)
⏳ Duration: 3 years
🗓️ Application deadline: June 15, 2026
PhD Topic: Hard and Soft Constraints in Scientific Machine Learning (SciML)
The project focuses on one of the key challenges in Physics-Informed and Scientific Machine Learning: How should physical constraints be integrated into machine learning models?
We will investigate and compare:
- Soft constraints (loss-based penalties)
- Hard constraints (architectural or solver-based enforcement)
- Hybrid approaches combining both paradigms
The research will involve:
✅ Physics-informed ML and system identification
✅ Dynamical systems and port-Hamiltonian formulations
✅ Benchmarking and evaluation protocols for constrained learning
✅ Applications in:
- Soft robotics (HASEL actuators)
- EEG and neuroscience data analysis
The ideal candidate would have a strong background in:
- Machine Learning
- Control Theory
- Applied Mathematics and Scientific Computing
Strong Python/ML programming skills and prior research experience are highly appreciated.
Besançon offers an excellent research environment, with the CNRS-affiliated FEMTO-ST institute being one of the largest engineering research laboratories in France.
📩 To apply, candidates should send:
- CV (including projects and potentiel publications)
- Concise Cover letter (without LLM)
- List of References
to: karim.cherifi (at) supmicrotech.fr
Subject: [PhD position] Your Name
Please feel free to share this opportunity with interested students and researchers in SciML, control, and physics-informed AI communities.
