-
for Applied Power Electronic Systems. You will join an interdisciplinary team working across electrolysers, Power-to-X systems, power electronics, control, digital twins, and energy-system optimization, in
-
scientific AI, system modelling and identification, as well as optimization and control. Your work will focus on developing new methods that integrate first-principles models with data-driven learning
-
perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
-
optimization, techno-economic assessment and model validation. The research will also examine the coordinated operation and control of Power-to-X processes, electrolysers and energy-storage systems in
-
Technology, and the PhD student will be positioned in the Esbjerg Energy section. The position is part of the internally funded research project SURGE: Speed-optimized USVs for Robust Guidance and Offshore
-
world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and