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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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you as a scientist and a person; Your course list and grades from your university education; a copy of your diploma (BSc/MSc/PhD – in English; your own translation is acceptable at application stage
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and PhD degree certificates or equivalent (copy of original/official English translation). Complete list of publications. Publications most relevant to the position. Summary and documentation
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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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. A scanned copy of the final degree (PhD or equivalent) certificate. If the degree is not yet awarded, it is sufficient to mention in the cover letter when the applicant expects to submit the thesis
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maintenance test facility at SDU. This includes dynamical simulation of breeding blanket extraction with a robot, design and implementation of real-time control system, selection of sensors and actuators
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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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main campus. Depending on the position, the work will involve a combination of experimental research, process simulation, reactor operation, and system-level optimization. We are currently advertising