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Old Road Campus, Headington, Oxford, OX3 7LF Oxford Population Health (the Nuffield Department of Population Health) provides an excellent environment for multi-disciplinary research and teaching and for professional and support staff. We work together to answer some of the most important...
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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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, scientific machine learning (ML), wind energy, and advanced optimization? Join our team to develop cutting-edge solutions for aerodynamic design optimization of wind energy systems in complex urban
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Are you an innovative researcher with a strong background in CFD, scientific machine learning (ML), wind energy, and advanced optimization? Join our team to develop cutting-edge solutions
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position within a Research Infrastructure? No Offer Description Aalto University is looking for an Postdoctoral Researcher in Artificial Intelligence / Machine Learning Engineering [Academic Research
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other data scientists to advance precision medicine research. Your main tasks and responsibilities: Designing and leading analyses that apply state-of-the-art generative machine learning models (e.g
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and data structures for AI kernels; Scalable systems for machine learning (training, inference, edge); HW-SW co-design for computer vision on novel architectures; Desired skills Advanced knowledge in
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, United States of America [map ] Appl Deadline: (posted 2025/09/04, listed until 2026/02/20) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | 4 minutes ago
such as electrical power. Recent advances in machine learning present new opportunities to enhance the level of fault management and control in NASA's future power system applications. This work aims to