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be finalised by July 2026. Supported Areas for START Engineering Energy Resilience: Power electronic converters for distributed energy resources and microgrids; High voltage DC transmission and
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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of Physics and Astronomy. AIPAD tackles the above questions by developing two innovative AI algorithms: The first algorithm will infer full SEP pitch-angle distributions (PADs) for spacecraft measurements
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dozen input variables). The foreseen approach would be to build on recent developments in using CNNs for Species Distribution Models (e.g. Deneu et al 2021, Morand et al 2024) to summarise the complex
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on the data recorded in the team, you will develop and test machine learning algorithms for perovskite tandem solar cells' energy yield and degradation Data cleaning and preparation Assisting integration
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, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation group. Main responsibilities Conduct research in collaboration with senior researchers and
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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subcellular distribution of mechanosensitive membrane proteins. The successful candidate will have the opportunity to work with a dynamic team of researchers in a state-of-the-art facility equipped with
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute