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Closing date: 15th January 2026 Background We are seeking applications for a part time research fellow (70%fte) to work on an NIHR Public Health Research Programme funded project for 12 months. The project
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. The incumbent will actively contribute to the design, development, delivery, review, and continuous improvement of resources for the Innovation Capability Program and associated short courses. Additionally, in
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, or district energy systems. Experience with system optimisation and techno-economic analysis of energy systems. Proficiency in scientific computing environments such as Python, MATLAB, Julia, or Modelica
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. Must have experience with analysis of sequencing data (RNA-seq, CUT&RUN, etc) and/or sufficient computation background to learn these analyses. Must have a strong publication history supporting potential
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topological defects and alignment patterns The project offers close interaction with experimental biology, theoretical physics, and computational modeling in a strongly interdisciplinary research environment
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manufacturing capabilities, and high-performance computing resources. The ideal candidate will have a track record of high-impact first or lead-author publications, demonstrable potential for independent research
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researcher with a strong interest in optical fibre technology to work on the UK’s Engineering and Physical Sciences Research Council (EPSRC) funded Programme Grant, HiPPo (Smart Fibre optic High-power
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to appoint one or more postdoctoral fellows beginning in November/December 2025. We are looking for candidates with interests in the use of atomic cavities and atomic arrays for quantum computing. Candidates
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. Experience working with medical images. Very good computer programming skills and physics background is essential. Desired Qualifications* Nuclear medicine imaging/dosimetry experience. Experience in image
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, CRISPR genome editing, CUT&RUN/ChIP, scRNA-seq/scATAC-seq, super-resolution/expansion microscopy, calcium imaging, proteomics, hydrogel fabrication/characterization, bioprinting and computational modeling