168 machine-learning-phd-"The-Art-Institutes" Postdoctoral positions at University of Oxford in United Kingdom
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(DataAcq) project. This is a timely project developing new methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related
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We invite applications for the position of Postdoctoral Research Associate in Machine Learning/Machine Learning Scientist to join the Deep Medicine programme at the Nuffield Department of Women’s
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genomics/transcriptomics, computational biology and machine learning. The overarching aim of the project is to develop strategies to treat patients with rare genetic disorders at scale. Duties will include
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We invite applications for the position of Postdoctoral Research Associate in Machine Learning/Machine Learning Scientist to join the Deep Medicine programme at the Nuffield Department of Women’s
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for privacy, security and data-richness. You should possess a PhD or DPhil (or near completion of) in Computer Vision or Machine Learning. You should have knowledge of approaches for areas related to efficient
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relevant PhD/DPhil (or near completion*) in Computer Vision or Machine Learning. You should have a strong publication record at the principal international computer vision and machine learning conferences
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and educational contributors to early mathematical learning for neurodivergent children. Our focus will be three groups of children with genetic conditions diagnosed early in life (Down’s syndrome
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statistical/computational genomics, machine learning, computer science, bioinformatics, epidemiology, or related field with relevant experience. You will have excellent communication skills, including
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or Machine Learning. The University of Oxford, supported by funding from Google DeepMind, has created a three-year postdoctoral role in AI or ML that will focus on an independent research agenda and also have
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We are looking to appoint a research scientist to carry out research in machine learning and artificial intelligence to develop interpretable clinical decision support tools and novel methodologies