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works on the future of our power system. Job requirements We are looking for candidates who bring: A PhD in electrical engineering, power systems or a closely related field, obtained or close to
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude for team work and excellent communication skills in spoken and written
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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machine learning for molecular and material design; quantum computing for bioinformatics; quantum approaches for safe and sustainable molecular design; and benchmarking quantum simulations of materials
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(there is room for learning-on-the-job). A PhD in aerospace/mechanical engineering or applied physics. Demonstrated ability to conduct research in experimental fluid mechanics. Proven competence on flow
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cardiovascular care. Within the consortium, TU Delft contributes expertise in cardiac mechanics, soft tissue modeling, growth and remodeling, machine learning, and uncertainty-aware model personalization. As a
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sustainable crop management. Your colleagues: You will be based at BFFI in Venlo, a collaboration between BASF and the Maastricht University Faculty of Science and Engineering. At BFFI, researchers and students
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germanium, and executed quantum algorithms on a four-qubit quantum processor. As a Postdoc researcher, you will explore opportunities with semiconductor quantum technology and aim to push the boundaries in
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch