182 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of Oxford in United Kingdom
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collaborative programme bringing together a team of leading experts in advanced electron microscopy imaging, first-principles modelling, metal halide semiconductor thin-film and device fabrication, and
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The University of Oxford is seeking a highly motivated Postdoctoral Scientist with expertise in biostatistics, machine learning, and cardiac magnetic resonance imaging (MRI) to join Professor Betty
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months), and is full-time. Applicants will have, or be close to completing a PhD/DPhil in statistics, computer science, or related areas. They will have excellent communication skills, including
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We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans and machine learning systems led by Prof Chris
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against existing imaging data. The group’s long-term vision is to impact clinical practice by identifying characteristic manifestations of age, trauma, and disease on the brain’s form and function. You will
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing
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Disease Research Programme. This role focuses on advancing early diagnosis and risk stratification in individuals with inherited cardiac conditions through cutting-edge imaging research. The successful
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other postdocs, research assistants and PhD and project students. The research group aims to apply the latest advances in molecular biology, immunology and neuroscience to better understand the mechanisms
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About the role This is an exciting opportunity to work at the forefront of a new area of research exploring the interaction between avian vision and collisions with anthropogenic objects