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the different organelles. Another approach is to use Monte Carlo simulations to explore which gene regulatory network architectures are necessary for robust regulation and effective communication between
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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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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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spend some time on placements in the industrial partner. We are seeking ambitious, inquisitive and innovative individuals to join our multidisciplinary research team. As part of a dynamic and
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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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time-scales: ab initio methods for the description of reaction processes, for the determination of electrochemical stabilities and for the optimisation of force fields; molecular dynamics simulations