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during downstream processing. Theoretical investigation of molecule stability during DSP, using molecular interaction simulations such as e.g. Molecular dynamics (MD). Combination of PAT and data driven
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, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery
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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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as ligands. The fundamental science behind the selective enrichment of lithium-6 by solvent extraction is poorly understood. This project will combine molecular quantum mechanics and molecular dynamics
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dynamics of plasmid copy number in pathogenic bacteria, with a strong focus on infection biology and evolution of antibiotic resistance. The goals for this project are i) to better understand the role
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and motivated theoretical biological physicists who ask how physical mechanisms shape functional biological patterns. We combine statistical physics, nonlinear dynamics, mathematical modeling and data
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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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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