340 phd-position-computer-science-"IMPRS-ML"-"IMPRS-ML" positions at University of London
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dynamics simulations to recover dynamic strain and flow fields. Candidates should hold a PhD in a relevant biology or engineering discipline and be competent with numerical simulations. We are looking for a
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researchers and industrial collaborators on the research project. About You The candidate should have a PhD (or close to completion) in a biological, biomedical or closely related science. Previous work
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the Department of Biological Sciences. This is a part-time role at 0.5 FTE and is for the fixed term of 36 months. The position is available from 1 October 2025 or as soon as possible thereafter. A unique
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September 2025 Reference 0925-207 Right to work: Please note that it will not be possible for the University to issue a Certificate of Sponsorship to the successful candidate for this position. Therefore
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that changes to mechanical sensing, signaling and memory, critically influences the disease onset and progression1. The Iskratsch Group , at the School of Engineering and Materials Science, Queen Mary University
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oversight of a portfolio, you will gain keen insights into how various academic programmes are performing; and be able to work with the programme teams to ensure these programmes are functioning well and
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About the Role We are looking for a Teaching Fellow in Skills/Language for Science and Engineering to undertake a fixed term 2-year post in the Queen Mary Engineering School, in Xi’an, China to
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machine learning under real life constraints of clinical integration/validation and healthcare regulatory translation/commercialisation. The position is part of the project `Integrated autofluorescence
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About the Project We are seeking a talented and dedicated team of scientists, bioinformaticians and support colleaguesto join the ground-breaking PharosAI initiative – a £43.6M national programme co
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as