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at the Photonics & Nanotechnology group, Physics Department, King’s College London, funded by the EPSRC Programme Grant New perspectives in photocatalysis and near-surface chemistry: catalysis meets plasmonics —a
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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PhD (or be near completion) in a field such as AI, computer science, signal processing, biomedical engineering, have a strong record of publication in engineering or computer science literature, and
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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other partners in the project. About you You should possess a PhD/DPhil in Engineering, Computer Science or other related field, (with the possibility to underfill at Grade 6 (£34,982 - £40,855 p.a
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to artificial intelligence (AI) (e.g. computer science, engineering, Statistics, and mathematics etc.) The post is available for 30 months, starting on 1 September 2025. If you are still awaiting your PhD to be
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opportunities to develop research profile, to travel to give demos and presentations, and to write academic papers. Candidates should hold a PhD degree in Computer Science or other relevant discipline. The
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possess a PhD/DPhil in Engineering, Computer Science or other related field, (with the possibility to underfill at Grade 6 (£34,982 - £40,855 p.a.) if candidate holds a relevant degree and is working on PhD
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have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track