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approaches that predict the static structure of proteins have become invaluable tools for biologists and medicinal chemists, machine learning-based prediction of protein dynamics is still in its infancy
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in these environments is largely determined by supramolecular interactions, which are complex and challenging to predict, particularly in multicomponent systems. This collaborative project between few
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fulfil its biological function. While machine learning approaches that predict the static structure of proteins have become invaluable tools for biologists and medicinal chemists, machine learning-based
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-set that will prepare you for a future in research in a world where there is an increasing need for professionals with hands-on experience with complex assessment algorithms. This position is funded by
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prototypes currently being developed within the consortium. In close collaboration with senior lab members, you will use our multi-scale computational fluid-structure interaction framework that describes
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the MODES project. In this project, we develop a charcoal sketch of the future governance structure of the Dutch energy system together with a large set of stakeholders, ranging from regulation in
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the institute. The research programme is focused on synthesis, catalysis, functional materials, bio-organic chemistry/chemical biology and systems chemistry/complex molecular systems. The research program of
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and enjoy engaging young people in science, we invite you to join our team in unraveling the complexities of online behavior and shaping a smarter, safer digital future for adolescents. For more details
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complex scientific challenges related to data-intensive science, sustainability, HPC and AI; passionate about working collaboratively with other researchers, scientists and industry; a strong interest in AI