812 phd-studenship-in-computer-vision-and-machine-learning PhD positions in United Kingdom
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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in
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, in addition to interfacing with the established Human-Computer Interaction (HCI) FIT lab. The candidate will be working alongside other researchers on the FLUIDITY project on human-robot interaction
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The School of Computing at the University of Kent and Huawei invite applications for 2 fully-funded 3.5-year PhD scholarships for Home and Overseas students. You can apply for a PhD on the topic
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This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to investigate the conditions that support forest recovery after a fire. The study areas can be defined using...
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The Topic This PhD studentship is based in the School of Psychology at the University of Kent. The studentship is funded by a European Research Council Starting Grant fulfilled through a UKRI
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materials. The computer modelling of LSP remains challenging due to its multi-physics and multi-scale nature. The dependency of the process on the shape of the laser pulse, its energy, ablation layers etc. is
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part of the larger project. The scope of the PhD project is to implement, use, and where required develop, statistical machine learning tools to identify DNA mutations that cause particular types
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Please quote reference: Quantum Computing at The School of Physical Sciences Duration: 3 years and 6 months. We are currently recruiting for the following full-time, fully funded PhD studentship
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability
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to date focus on just one layer, understanding what keeps AF going is challenging. This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol