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large-scale omics datasets, develop and apply statistical methods and interpretable AI models, and contribute to the identification of biological markers and molecular mechanisms associated with disease
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duration of the education, which corresponds to four years. Position description As a doctoral student, you will conduct research focusing on the development and application of AI-based methods for energy
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motion in non-classical geometries using combinatorial, algebraic, and group-theoretic methods, inspired by the mathematical theory of articulated systems and constraint geometry. Possible tools include
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out together with seven industrial partners and is externally funded by the Knowledge Foundation. In co-production with our corporate partners and the community, we develop concepts, principles, methods
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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qualifications: The successful candidate must: Hold a PhD in History or Economic History. Have experience of working with both quantitative and qualitative research methods. Have published research relevant
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preferably experience working with cattle. Documented experience in data collection, processing, and management of data from animal feeding trials. Experience with wet chemistry methods for feed analysis
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student will use and develop both computational and laboratory-based tools. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and
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in quantitative and qualitative analytical methods and, where relevant, AI-based methods carry out data collection, which may include qualitative interviews with patients or healthcare professionals
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following methods: microscopy, epigenome analysis, molecular biology techniques or working with Arabidopsis. documented knowledge in a relevant field of research capacity for analytical and creative thinking