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The GW4 BioMed3 Doctoral Landscape Programme is offering up to 18 MRC funded studentships across a range of biomedical disciplines, with a start date of October 2027. These four-year studentships
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The GW4 BioMed3 Doctoral Landscape Programme is offering up to 18 MRC funded studentships across a range of biomedical disciplines, with a start date of October 2027. These four-year studentships
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particular emphasis on applications from psychology, cognitive science and computer science, and the candidate will be encouraged to engage in interdisciplinary collaboration and communication. We are looking
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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collaborative, interdisciplinary environment that brings together expertise in chemistry, physics, and computer science. Success in this role requires the ability to engage effectively with colleagues from
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for candidates with A master’s degree (e.g. MSc, MEng, MPhys, MRes or equivalent) in a relevant engineering or physical sciences discipline, such as electrical/electronic engineering, general engineering
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The GW4 BioMed3 Doctoral Landscape Programme is offering up to 18 MRC funded studentships across a range of biomedical disciplines, with a start date of October 2027. These four-year studentships
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
class or 2:1 degree in the field of Cognitive Psychology, Human Factors, Ergonomics, Human-Computer Interaction, or related behavioural science. An MSc degree in a relevant area is desirable though not
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-026-01104-3. Preferred skills/knowledge Applications are invited from graduates in quantitative disciplines such as computer science, AI, and mathematics, but we also encourage applications from
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Engineering, Control Engineering, Mechatronics, Robotics, or a related discipline with a strong focus on dynamic systems. Strong background in state-space modelling, estimation theory, and control systems. Good