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molecular docking, molecular dynamics and free-energy methods (MD/FEP), machine learning for molecular design, and protein–ligand modelling. Experience bridging computational and experimental groups, and the
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Assistant Professor position in Computer Science with a Focus on Interdisciplinary Data Science_3...
enable advanced analytics across disciplines. Experience in developing and deploying machine learning solutions is considered an advantage. Experience applying such solutions within digital humanities
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for this PhD proposal should have the following qualifications: M.Sc. degree in electrical engineering, mathematical engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills
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at the BEng, BSc, MSc, and PhD levels. In addition, there will be an obligation in continuous education on advanced machine learning methods and AI. The Section for Cognitive Systems has a strong interest and
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engineering applications at the BEng, BSc, MSc, and PhD levels. In addition, there will be an obligation in continuous education on advanced machine learning methods and AI. The Section for Cognitive Systems
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quantitative analysis of register, survey, and patent data to various qualitative methods. If you have an interest in, or experience with, novel computational methods such as NLP, machine learning, and AI
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testing and condition monitoring using modern machine learning, including multimodal foundation models and related data-driven and physics-informed approaches. Research topics may include visual and real
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vision, XR and generative models, specifically for capturing challenging scenarios and training deep learning systems to create better experiences for human users and learners. You will contribute
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for farm-farm interaction Development of coupled LES and aero-elastic models using the actuator line method Analysis and design of wind farm control through LES and machine learning Scientific publication
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and