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ERC-funded postdoctoral fellow in theoretical developmental biology, using tools from applied mathematics, biophysics, and machine learning A talented and creative researcher is sought to take part
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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: Statistical signal/image processing, deep learning, machine learning, neuromorphic computing Good communication skills and an appropriate publication record are essential. Solid knowledge of Python and C++ is
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Vision or Machine Learning. You should have a strong publication record at the principal international computer vision and machine learning conferences and should hold sufficient theoretical and practical
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scientists, and researchers working on medical image analysis, machine learning, and audiology. Our recent work has focused on using deep learning to analyse temporal bone CT scans and brain MRI data in
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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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and motivated candidates for a postdoctoral positions working on cutting-edge research at the intersection of Machine Learning, Privacy-Enhancing Technologies, and Public Interest Technology. We
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning