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Field
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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used to measure motion and deformation. It provides comprehensive full-field deformation data, essential for analysing complex materials, structures and model validation. The DIC community has developed
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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. Clinical studies have demonstrated that hereditary neuropathies can also involve muscle pathologies, complicate diagnosis and hamper therapy development. We have developed advanced in vitro cell models
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the Computer Science study program. The stipend is open for appointment from August 1st 2025 or soon thereafter. The PhD students will be working on topics within the general areas of formal methods, model checking and