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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
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(e.g. using COBRApy or related toolboxes), or a strong motivation to develop this expertise. Data science, AI/ML, and digital surrogate models Experience with data science and machine learning, including
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extensive experience working with large data sets in Python are required for the position. Experience with machine learning, OCR, natural language processing, geospatial analysis, and data visualization is a
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science to identify and assess usecases and formulate them as problems solvable on a quantum computer Interest in developing strategies for optimal communication of the results of their research with
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requirements, including link budgets, beam steering, and orbital pointing dynamics. • Experience with optimization methods and physics-informed machine learning. • A strong publication record in antennas
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research profile within organisational studies, Computer-Supported Cooperative Work, Human-Computer Interaction or related research areas as documented by a PhD dissertation and/or research publications
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experimental and suited for candidates who enjoy hands-on research, learning new techniques, and working across disciplinary boundaries. Your competencies We seek a highly motivated candidate with a strong
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sedimentary archives, to facilitate and use in-situ and remote sensing observations of polar environments, and to acquire skills within VibroSeismic data acquisition, analysis and interpretation. The position
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are expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The
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expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The Department