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applicant will work mostly within our Technology Assessment Review programme, undertaking research to support National Institute for Health and Care Excellence (NICE) decision making, and in our NIHR Evidence
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the objective of discovering computational theories of dynamical systems. The post holder will report to the Principal Investigator. The post holder will be a member of research groups within the project, with
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biggest challenges. We are seeking a Research Fellow in Liquid Metal Artificial Enzymes to join the School of Electronics and Computer Science (ECS) at the University of Southampton. The successful
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culture, you’ll play a key role in postgraduate and doctoral teaching, research supervision, and contributing to the PsychD programme and wider School. Experience in qualitative, quantitative, or mixed
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of technology which integrates robotics, computer vision, and data infrastructure to enable scalable, reproducible and data rich transformation platforms, supporting the team with subject matter expertise
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English language qualifications for entry to this programme TOEFL (ibt, mybest or athome) Tests taken up to 20 January 2026 90 Overall, no subtest lower than Reading 20; Listening 19; Speaking 19; Writing 21 Tests
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contribute to teaching students in the School’s undergraduate programme in Paediatric Dentistry. You will participate in the maintenance and further development of excellence in the quality of education within
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disciplines: Advanced Computer Science MSc Chemistry MSc by Research Comparative Social Policy MSc Economics for Development MSc Education (Digital and Social Change) MSc Education (Higher Education) MSc Energy
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. To fill in this gap, in collaboration with industrial partners, the research will develop novel Machine Learning and Computer Vision methods for detecting and localising. These will be used to develop
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endemic countries. We are seeking to appoint a Research Fellow to join a research programme that applies advanced bioinformatic, statistical, and population genomic approaches to large-scale sequencing data