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developing custom Python-based simulation scripts. TU Delft will support your research with access to the DelftBlue supercomputer for large-scale and intensive modelling. In the second phase, you will work
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will computationally explore catalyst materials for synthesizing different types of hydrogen storage molecules. Using advanced quantum mechanical calculations, you will develop multi-scale models
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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programming (e.g. in Python), or the willingness to learn; the willingness to work collaboratively with other researchers; professional command of English and good presentation skills. This is what we offer you
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Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences. If you consider that you do not meet all the requirements, we encourage you to
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laser physics and/or plasma and fluid mechanics is an asset, especially if combined with strong hands-on laboratory skills Programming experience, particularly in Python, is welcomed Strong verbal and
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English is not your mother tongue; Official, stamped MSc degree certificate; Good command of programming languages like Python will be considered as a plus; Experience with sorption behavior of surfactants
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is possible in collaboration with other members of the lab. Desired (but not absolutely required) skills: programming in python, machine learning, and experience in protein structure analysis. Required