33 phd-studenship-in-computer-vision-and-machine-learning PhD positions at University of Cambridge
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Applications are invited for a fully-funded 3-year PhD studentship based in the Department of Clinical Neurosciences at the University of Cambridge under the supervision of Professor Stephen Price
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cutting-edge advanced materials fabrication, simulation and characterization to address the pressing challenge of achieving a sustainable IoT ecosystem. The doctoral candidates will acquire a solid
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catalytically active metals to drive chemical reactions with light [3-4]. The specific goals of this PhD project are to 1) understand how plasmonic Mg nanoparticles and their surface oxide layer attract and
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the University's Applicant Portal for a PhD in PhD in Medicine. Please apply via the application portal here - https://www.postgraduate.study.cam.ac.uk/courses/directory/cvmdpdmed Please quote reference RC45508
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We are seeking an applicant for a fully-funded ERC Research Assistant position with the opportunity to undertake PhD studies in statistical methodology and theory led by Professor Richard Samworth
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AHRC Collections & Communities in the East of England Collaborative Doctoral Partnership (CDP) PhD studentship: Reimagining Caribbean Collections: Unveiling Histories of Identity and Wellbeing
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Applications are invited for a fully-funded 3-year PhD studentship based in the Department of Clinical Neurosciences at the University of Cambridge under the supervision of Dr Topun Austin starting
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Fixed-term: The funds for this post are available for 4 years in the first instance. AHRC Collections & Communities in the East of England Collaborative Doctoral Partnership (CDP) PhD studentship
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for the award, applicants must be accepted onto the doctoral programme. Candidates must apply for the PhD in Social Anthropology through the University's Graduate Admissions application portal by no later than
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Transactions on Probabilistic Machine Learning. A Gelman, A Vehtari, D Simpson, CC Margossian, B Carpenter, Y Yao, L Kennedy, J Gabry, PC Bürkner, M Modrák (2020). Bayesian Workflow. B Carpenter, A Gelman, MD