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. The outstanding research projects analysing the molecular, cellular and organismal basis of infections and immune reactions in our graduate school programme are supported by a flexible curriculum structure
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
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tools from chemistry and biology, and apply these in studies of therapeutic peptides and proteins. Our aims are to develop modulators for protein-protein interactions (PPIs) and to provide molecular-level
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resistance potential of ash trees. The project aims to support conservation efforts by refining selection criteria for resistant ash based on a comprehensive understanding of disease dynamics and environmental
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of computational chemistry. Applicants can have a background from cheminformatics including RDKit, machine learning applied to chemistry, and molecular modeling Our group and research- and what do we offer? Our
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implementation is advantageous (especially if in the context of protein structure) Experience with structural biology and/or molecular dynamics is advantageous Publicly available code is advantageous Experience
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, supporting infrastructure, media room for the production of science communication audio and video projects List of doctoral programmes and other offers For an overview of all graduate schools and structured
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results, further research is guided by trial and error with the goal of deriving intuitive trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular