12 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at University of Cambridge
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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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The Faculty of History and Christ's College invite applications for a three-year fixed term Fellowship, as part of the Isaac Newton Trust's Academic Career Development Fellowship programme. The
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of the following: A two-page CV, including the dates on which you had your PhD viva and on which you were awarded the PhD. A list of publications (not exceeding one side) NB: DORA principles should be considered
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, with the aim of building long-term institutional capacity in this area. Candidates should have a PhD (submitted or nearly submitted at the time of application) in a field relevant to urban studies
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advantageous. The role comes with the opportunity to undertake a fully funded PhD, based within the Department of Medicine, and with access to high quality training and mentorship. Informal enquiries regarding
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for someone who is educated to degree level, normally with a PhD (or very close to being awarded a PhD) in a relevant discipline, e.g. Modern History of Asia/Modern World History. An established expertise in
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date of PhD (or anticipated date of submission/examination), and a list of publications and presentations; 2) Proposed mentor within the Faculty, and whether that mentor has been approached; 3) Title
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subject who have the ability to lead an exciting, innovative and fundable research programme. The applicant would typically have at least 3 years of post-doctoral experience and may already have experience
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programme of support that will help you to establish you as an independent academic researcher, including assistance with your fellowship application, setting up your lab and recruiting personnel, as
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access, and work with other curators, collections management staff, photographers, the documentation team, conservators, scientists, learning staff and other colleagues across the Museum and the wider