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multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery. Project details This PhD project will contribute to the development of generative models for multimodal data
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Department/Location: Yusuf Hamied Department of Chemistry Applications are invited for a 4-year PhD studentship based in the Department of Chemistry, University of Cambridge and the new AstraZeneca
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PhD Studentship: Gas Transport in Polymeric Materials for Defence Applications The sustainable use of polymeric materials is underpinned by ensuring long service life in all industries and
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a 300-word statement explaining your motivation for applying to this PhD Studentship to: Supervisor Review Form . Our supervisors will perform a comprehensive review to long-list candidates. Deadline
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inquiries can be made to ian.sayers@nottingham.ac.uk Application details: to apply for this PhD opportunity, please submit the following documents to ian.sayers@nottingham.ac.uk Cover letter outlining your
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Deadline: Monday 15th December, 2025. Competition funded PhD Project. Supervisors: Dr. Chih-Jen Lin (The University of Edinburgh), Dr. Pierre O. Bagnaninchi (The University of Edinburgh) About
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The development of sustainable and efficient powertrain systems is central to achieving global decarbonisation targets in transport. Professor Xu has long-standing expertise in internal combustion engines, alternative fuels, combustion optimisation, and powertrain control. As the automotive...
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King’s Business School Doctoral Research Studentships King's Business School invites applications for funded, full-time PhD studentships in Management Studies , to start in October 2026. We welcome
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-efficient and high-performance photonic devices have been driven by the quantum revolution. This PhD studentship aims to develop novel materials and components that facilitate strong light-matter interactions
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined