France: PhD position in Scientific Machine Learning (SciML)

๐—Ÿ๐—ผ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป: Besanรงon, France
๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ฑ๐—ฎ๐˜๐—ฒ: September 2026 (flexible)
๐——๐˜‚๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป: 3 years
๐—”๐—ฝ๐—ฝ๐—น๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฑ๐—ฒ๐—ฎ๐—ฑ๐—น๐—ถ๐—ป๐—ฒ: June 15, 2026

๐—ฃ๐—ต๐—— ๐—ง๐—ผ๐—ฝ๐—ถ๐—ฐ: ๐—›๐—ฎ๐—ฟ๐—ฑ ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐—ผ๐—ณ๐˜ ๐—–๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐—ถ๐—ป๐˜๐˜€ ๐—ถ๐—ป ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐˜๐—ถ๐—ณ๐—ถ๐—ฐ ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด

The project focuses on one of the key challenges in Physics-Informed and Scientific Machine Learning: โ€œHow should physical constraints be integrated into machine learning models?โ€

The research will involve:
- Physics-informed ML and system identification
- Dynamical systems and port-Hamiltonian formulations
- Benchmarking constrained learning methods with applications in soft robotics and neuroscience

The position is ideal for candidates with a strong background in:
โ€ข Machine Learning
โ€ข Control Theory
โ€ข Applied Mathematics and Scientific Computing

Strong Python/ML programming skills and prior research experience are highly appreciated.

To apply, candidates should send: a CV, Cover letter and References to: 
karim.cherifi (at) supmicrotech.fr with the Subject: [PhD position] Your Name

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