584 computer-programmer-"https:" "https:" "UNIS" "https:" "https:" "https:" Fellowship positions in United Kingdom
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are essential for the role. In order to help to develop the programme of genetics at City St. George’s, we particularly welcome applicants who can demonstrate academic ambition, initiative and independent
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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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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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wider research culture of the University. A key component of the role is the opportunity to develop your own programme of research. Applicants are therefore required to submit a research proposal (1,500
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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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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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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