Multiscale SciML Digital Twin for High-Torque Propulsion

University of Exeter; Exeter; On-site; GBP 20,000 - 24,000; Full time

Job summary

University of Exeter invites applications for a doctoral studentship focused on multiscale scientific machine learning for predicting thermal trajectories, torque capability and efficiency in low-speed, high-torque propulsion systems. The project blends physics-informed neural networks, recurrent models and Neural ODEs, with digital-twin validation and experimental data from a high-performance dynamometer.

You will collaborate with Ecomar Innovations on industrial hardware, spend significant

Job description

University of Exeter invites applications for a doctoral studentship focused on multiscale scientific machine learning for predicting thermal trajectories, torque capability and efficiency in low-speed, high-torque propulsion systems. The project blends physics-informed neural networks, recurrent models and Neural ODEs, with digital-twin validation and experimental data from a high-performance dynamometer.

You will collaborate with Ecomar Innovations on industrial hardware, spend significant