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ENSAM
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Postdoctoral Position: Hybrid Hydrodynamic Modeling and Nonlinear Model Predictive Control (NMPC) for Unmanned Surface Vehicles (USV) ENSICAEN in France
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Dec 4, 2026
Country
France
University
ENSAM
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About this position
This postdoctoral position at ENSICAEN is part of a collaborative ANR project involving the LIS laboratory (UR 7478, Caen) and the company NEAC. The project aims to develop an innovative bilateral teleoperation architecture for surface vessels operating under degraded communication networks.
The postdoctoral researcher will focus on the autonomy and control foundation of an unmanned surface vehicle (USV), specifically the local and embedded control architecture needed for constrained environments such as shallow waters, canals, and port areas. A major part of the work is the design of a Nonlinear Model Predictive Controller (NMPC) coupled with a hybrid hydrodynamic model that blends classical physics with artificial intelligence methods, including gray-box modeling and Physics-Informed Neural Networks (PINNs).
The research is centered on accurately representing complex maritime perturbations such as bank effects and squat, then using those models to achieve precise and robust trajectory control. The developed controller will serve as the autonomous navigation layer for the vessel, supporting later integration into the broader bilateral teleoperation system.
The project also involves real-time mathematical optimization, embedded deployment on a dSPACE MicroAutoBox III target, and validation of the models during full-scale vessel trials. The work sits at the intersection of control engineering, marine robotics, hydrodynamic modeling, and experimental validation, in close collaboration with academic and industrial partners.
Funding details
Available
What's required
The candidate must hold a Ph.D. in automatic control, control theory, applied mathematics, or robotics. Advanced and proven expertise in Model Predictive Control (MPC/NMPC) or constrained optimization is required, along with solid skills in modeling complex dynamic systems. Experience with machine learning techniques such as neural networks and PINNs for system identification is a major asset, and prior knowledge of naval hydrodynamics or marine robotics is a plus. Strong technical skills in MATLAB/Simulink and programming in C/C++ and Python are expected, as well as experience with real-time implementation on industrial computers such as dSPACE or Speedgoat. The project also requires a strong interest in experimental deployment and fieldwork, scientific autonomy, initiative, and good interpersonal skills.
How to apply
Apply through the Beetween application page provided in the advert. You may also contact the recruitment email at [email protected] for application-related questions. Use the official job link listed in the posting to submit your application.
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