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Field
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Requirements We will only consider candidates with a good Bachelor’s degree (ideally, a first, minimum 2:1) and/or a master’s degree in computer science, data analytics, health economics, or computational
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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The role The post is part of the Wellcome Discovery Award research programme “Collective Action for Race Equity in Health and Social Care”, which aims to address and dismantle structural factors
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Background Earthworms are synonymous with our notion of beneficial soil biology playing a key role in maintaining a healthy functional soil. Earthworm populations are estimated to have declined by a
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Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations with the Aerospace Integration
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of this contribution, and against the eligibility criteria listed below. As part of this programme we are able to offer several full-fee scholarships to professional learners from across Wales, working with
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PhD Studentship: Revolutionising Litz Wire Development for Next Generation Ultra-High Speed Propulsion Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers
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• Strong interest in interdisciplinary research Qualifications Research Assistant • Honours degree in (Biology, Neuroscience, Computer Science, Engineering, Mathematics, or related area) In addition
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Physics, Theoretical physics, Maths or other closely related subject (e.g. Materials Science with evidence of strong computational background and skills). To apply, please contact the supervisors; Dr Thomas