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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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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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partner in an exciting 5-year collaborative program. We are currently seeking applicants for three PhD projects as listed below. The successful candidates will be based in either NIBRT or UCD and will also
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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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equipment e.g. STM. Simulating fabrication methods. Collaboration with other groups at NQCP and companies/academic groups in and around the Copenhagen area. Join us in this major confluence of exciting
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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