494 computer-programmer-"https:"-"Prof" "https:" "https:" "https:" "https:" "https:" "University of St" Fellowship positions in United Kingdom
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About the job: As a part of the 14CHRONO team, the research fellow will lead a programme of research and development into novel thermally-driven evolved gas analysis (EGA) molecular separation
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University Situated in the heart of the historic city of York, with the addition of our London Campus, York St John University has a long and proud tradition as a distinguished higher education provider. Our
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major themes—cardiovascular science, neuroscience and immunology—addressing key organ systems targeted by non-communicable diseases. About the role The IDRM Transition Fellowship programme aims to support
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develop foundational security theory for agentic and multi-agent AI systems, as part of our research programme in multi-agent security. You will build formal models of security-relevant behaviour in
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recognised authority in dynamic-compression science and high energy density physics. They will be expected to be capable of developing their own programme of independent externally funded research projects
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by undertaking a specified range of activities within an established research programme and/or specific research project. Role Summary Work within specified research grants and projects and contribute
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candidate will contribute to an interdisciplinary research programme focused on the design, synthesis, and characterisation of liquid metal nanomaterials as catalytic artificial enzymes for biomedical and
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candidate will contribute to an interdisciplinary research programme focused on the design, synthesis, and characterisation of liquid metal nanomaterials as catalytic artificial enzymes for biomedical and
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suitable for a trainee with relevant experience in cancer clinical research as out of program experience or for someone wishing to pursue a career in these specialties prior to obtaining a training number
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during