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-film solar cells. LightTrap covers the entire discovery chain, from ab initio calculations and photonic simulations to materials synthesis and nanostructuring, all guided by advanced AI algorithms
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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the experiments. You will also have the opportunity to carry out your own simulations with our numerical model. Qualified applicants must have: A strong drive to move the frontiers of science. Ample experience with
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and MSc students. In the project you should: Design and implement enzyme libraries using generative AI tools such as RFdiffusion2 or BoltzDesign. Perform molecular dynamics simulations to assess enzyme
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molecular dynamics simulations and in silico screening to assess inhibitor-target interactions and predict selectivity. Clone, express, and purify top candidates using high-throughput bacterial systems and
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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning
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part of the project “SHORE: Simulating coastal HydrOdynamics and particle tRansport procEsses”, financed by the European Research Council. The SHORE project is briefly described below: Beaches
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university education; a copy of your diploma (BSc/MSc/PhD – in English; your own translation is acceptable at application stage; only if you are chosen for the position, do we need an official translation