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fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme! How does it work? Candidates will study for a four year, full time funded PhD
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
on integrated GRMs. The successful candidate will hold a PhD (or be close to obtaining) in Applied Physics, Material Science, Nanotechnology, Chemistry or Electrical Engineering with experience in photonics
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studentship will be hosted at the Institute for Aerospace Technology at the University of Nottingham and the candidate will sit within a community of PhD students at various stages of their study. The project
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provider: EPSRC DTP 25/26 Fully Funded Studentship Subject area: Algae biotechnology/computational biology/microbial community networks/ecological theory/evolutionary mechanisms/biomass productivity
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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the harmful effects of deepfakes on ordinary people. You will draw on methods from psychology, linguistics, data science, and computer science, such as behavioural analytics, neuroimaging, face and voice
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PhD Studentship: Microswimmers with Autonomous Propulsion Inspired by Biology and Designed with Physics Are you fascinated by working at the intersection of physics and synthetic biology? This PhD
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
system health monitoring, and more efficient maintenance planning. Digital twins offer a powerful foundation but must evolve beyond simulation to truly support engineering decisions. This PhD will develop