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largest protein/peptide mimotope dataset foundational for advancing any peptide-based drug discovery programme. The ERC PoC PepVerse project focuses on increasing the technology readiness of this platform
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We are seeking to appoint a full-time Postdoctoral Research Assistant in Wearable Technologies for Sport Medicine to work within The Podium Institute for Sports Medicine and Technology at
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to obtaining an enhanced DBS check, as required for working with children (A) This role offers the flexibility of hybrid working, opportunities for professional development, and the chance to
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About the Role We are seeking a highly motivated and skilled part time (0.75 fte) Research Assistant to join the Oxford School of Nursing and Midwifery. This is an exciting opportunity to contribute to an evaluation of how user-friendly and acceptable an AI-powered dashboard is among registered...
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semiconductor science at Swansea University’s new state-of-the-art Centre for Integrative Semiconductor Materials (CISM) and will contribute significantly to the EPSRC programme grant "Terahertz frequency devices
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scientific discipline. Experience in developing and running an atmospheric transport model. Experience in interpreting data. Experience with FORTRAN and Python computer languages. Proven ability to work
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join the project team in October 2025 or soon after. The candidate will join the Department of Engineering at King’s College London. They will become a member of the Centre for Robotics Research (CORE
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for data analysis/scientific computing, and excellent decision-making, problem-solving, planning, and organisational skills. The post is full time and fixed term until 30 June 2028. The closing date
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The Department of Engineering is recruiting a Post Doctoral Research Associate to join them on a fixed term contract. About the Department The Department of Engineering has approximately 60
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You will have a PhD in Computer Science or a related discipline or will have obtained it by commencement of the position. Successful candidates will have experience of model training methodologies