16 phd-computer-science-fully-funded-"IMPRS-ML"-"IMPRS-ML" Fellowship positions at University of Leeds
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have a PhD or be a PhD candidate who has already submitted your thesis prior to starting the position, with a research track record on artificial intelligence, data science and computational social
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project meetings. You will have a PhD in atmospheric science or climate science, including experience of running and analysing numerical models, and an appreciation of machine learning methods suitable
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should have a PhD (or close to completion) in structural biology or a closely allied discipline, with experience in cryo-EM, as well as protein expression and purification. The University of Leeds and the
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Universities? You will join a collaborative programme with Merck Electronics KGa, a world-leading company working in liquid crystals. You will work with a team of scientists from the company along with Dr
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Universities? You will join a collaborative programme with Merck Electronics KGa, a world-leading company working in liquid crystals. You will work with a team of scientists from the company along with Dr
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planning. You are expected to mainly work on the EU-funded project "ePowerMove: Enabling large e-fleets participation in power systems optimisation through mass deployment of people-friendly, low-cost
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Studies and Academic Unit of Health Economics, the University of Southampton, University of Birmingham and University College London (UCL). The post has funding for 18 months full-time equivalent and we
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to Power Agriculture, Clean Cooking and Transportation’ project could be the job for you. Moving IMPACT is a £3.6m research project funded by the Engineering and Physical Sciences Research Council
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looking for your next challenge? Do you have a background in social science and higher education research? Are you interested in working in an interdisciplinary team to understand the impact of curricular
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of the UKs leading research-intensive Universities? This role involves undertaking research on sustainable production and consumption patterns for a jointly funded project by UK Research and Innovation (UKRI