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theory, physics-based simulation, and machine learning. Job description The PhD project will develop machine-learning methods for atomistic materials modeling Possible research directions include machine
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the contribution; the possibility to follow courses to learn Dutch; Are you curious to read more about our extensive package of secondary employment benefits, take a look here. This is where you will work You will
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Our theory group is a worldwide leader in studying the statistical physics of living systems. We
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range of topics in numerical analysis and applied mathematics, including: Approximation Theory & Numerical Integration Mathematics for Data Science & AI Combinatorial Optimization Stochastics
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Afanasiev ([email protected] ) or dr. Carl Davies ([email protected] ). For more information about Utrecht University, see https://www.uu.nl/en , for more information about HFML-FELIX, see www.hfml
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(subject to positive intermediate evaluations). If the applicant is non-EER, up to 1 year of pre-doc will also be supported An exciting research environment, working on the intersection between theory and
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presentation of results at international conferences and publication in peer-reviewed journals are also expected About you: Advanced knowledge in control systems and feedback theory Programming skills in high
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description About the position The Department of Data Science at
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understanding of microstructure-property relationships in materials systems in the presence of different stimuli close to operational conditions. Such knowledge is crucial for designing advanced materials with
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at international conferences and prepare scientific publications Your Profile Master’s degree in physics or a closely related subject Solid background in quantum many-body theory and/or statistical physics