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on the electrical machine optimization to achieve high energy-efficient drive system. The practical part of the work will be testing the control model with a prototype machine in the research laboratory. Research
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. Advanced modeling techniques, such as surrogate modeling, machine learning, and physics-informed neural networks, will be applied to accelerate simulations and enable real-time performance. A strong emphasis
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goals Recent developments in autonomous driving have shifted toward E2E pipelines that unify perception, planning, and control into deep learning–based architectures. These models enable flexible decision
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD project aims to develop, model
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possible. The multi-dimensional nature of the model likely necessitates a machine learning based approach, yet should be minimally capable of running on the edge, ideally of running on power-efficient
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northern Europe. Our research covers a broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human
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performance in fuel cell (biogas) and co-electrolysis applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under
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of core web technologies. You have solid experience in Python and JavaScript programming languages. You preferably have experience with building and using machine learning models. You care deeply about