773 phd-in-computer-vision-and-machine-learning Fellowship positions in United Kingdom
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The University of Surrey is a global community of ideas and people, dedicated to life-changing education and research. We are ambitious and have a bold vision of what we want to achieve - shaping
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provide guidance to PhD students where appropriate to the discipline Contribute to developing new computational models, techniques and methods Undertake management/administration arising from research
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experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
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essential. Candidates are expected to be confident independent researchers, educated at a PhD level. The Research Fellow is also expected to assist with the general programme of work that is conducted in
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translate world-leading research on scientific understanding of disease mechanisms into innovative therapeutic approaches. Our vision is to play a leading role in discovering new medicines in areas of high
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global health, (medical) anthropology, mental health or applied health research to work on a major global health research programme on racism and social inequalities in mental health in Brazil. You will
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(R3) Country United Kingdom Application Deadline 23 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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and experimental approach to affective science. To be successful in this role, you should have: A PhD (or an equivalent level of professional experience) in an area of linguistics / pragmatics central
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Government policies and to inform innovation for industry leading to increased economic growth. Our main source of funding is from the Scottish Government funded Strategic Research Programme (SRP) which we
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. Required Knowledge, Skills, and Abilities: PhD or equivalent qualifications / experience in Experimental Photochemistry or related area. Preferred Knowledge, Skills, and Abilities: Experience in the field