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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
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2025. Encouraged by the continuing success of modern machine learning (ML) techniques, researchers have become ambitious to develop ML solutions for challenging science and engineering problems with
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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation