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scholarship is suitable for students with a background in Engineering, Mathematics, and Computer Science. Students with interests in machine learning, deep learning, AI, intelligent decision making
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About the role We are seeking a talented Postdoctoral Researcher with expertise in healthcare AI to join the Modernising Medical Microbiology (MMM) Unit at the University of Oxford. This is an exciting opportunity to lead innovative research at the interface of AI and clinical medicine, working...
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October 2026 start ONLY For January and April starts please use the relevant application. This form is only to be used by those self-funded applicants seeking a place on a research degree programme at
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of...
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University of Strathclyde will lead the wind energy training and research elements of the programme. Funded by ESB and EPSRC, this 4 year this PhD studentship, at the University of Strathclyde is in the area
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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Profiling hardware and feasibility of new
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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emerging photonic microdevices promising to revolutionise computer, communication, and sensing technologies must be performed with unprecedented picometre (one-hundredth of the atomic size) precision
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to apply, please use the below Programme Application Portal to get in touch with the Programme Admission Admin. Application received in other channels will be re-directed and requires apply for a second time
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability