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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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operate experimental facilities (electrolysis, PCM, BESS, etc.) and develop tools related to PtX testing, modelling, control, and energy system integration Co-supervise BSc, MSc, and PhD students and
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qualifications You must be well organized, structured, self-driven and enjoy interacting and collaborating with colleagues including PhD students, postdocs, and you are expected to take part in supervision of BSc
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, dynamic building simulations, experiments in climate chambers and field studies to evaluate the performance of developed control strategies. You will be involved in ongoing national and international
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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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and reduction of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts
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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