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-of-the-art AI methods, such as machine learning and knowledge-based methods, you will tackle high-dimensional biological data to uncover new insights into the connections between nutrition and health. In
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for the analysis and integration of –omics data. The group has a strong track record in (integrative) computational omics analysis, algorithm development, machine learning and scientific data infrastructure
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from machine learning methods to more traditional statistical and econometric techniques. We are driven by science with purpose, pushing the academic frontier by publishing at the highest level in
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problems, numerical mathematics, optimisation, machine learning and imaging physics, with applications ranging from medical and industrial imaging to geophysics. For more information, please visit
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self-assembly of ligand building blocks will be generated and explored. Detailed kinetic data gathered during these studies will also contribute to machine learning (ML) approaches in collaboration with
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facilities and access to microscopes etc. The BioMS group is world-renown for mass spectrometry-based proteomics and structural biology, and applies these technologies to study amongst others cancer, auto
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independently. We value personal development: you will receive training in advanced computational techniques, machine learning, data analysis and scientific communication. You’ll have the opportunity to attend
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Engineering or related disciplines, provided you have a strong interest in communication systems and networking. If you have a solid technical background and are excited about future wireless and satellite
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statistical analysis or computational modelling (e.g., growth curve modelling, machine learning, large language models); You have excellent English communication skills (oral and written). Basic Dutch
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or participatory research setting is a clear advantage; a completed (or nearly completed) Master’s degree in econometrics, mathematics, computer or biomedical sciences, artificial intelligence