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The purpose of this role is to support the work of the RICHeS programme at QUB providing research and access to archaeological and heritage science. The RICHeS Scientist (Research Fellow) will be a
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highlights the urgent need for novel approaches. Antimicrobial peptides (AMPs) are an emergent technology platform which are cost-effective to manufacture and have potential to control wound infection. Despite
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in physics, engineering, computer science, and mathematics is essential. Expertise in data analysis and inversion, Proven experience with geophysical, physical, or sensor data analysis, including
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-cell genomics/computer science (AI), neuroscience, vascular biology, developmental biology, cancer biology, immunology, biology/biomedicine, pharmacy, biomedical engineering, or related fields with ample
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Job Description Position Details Department of Applied Health Sciences, School of Health Sciences, College of Medicine and Health Location: University of Birmingham, Edgbaston, Birmingham UK Full
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, physics and astronomy, applied mathematics, statistics, computer science, etc.). The Research Associate will need to be proactive, working both independently and as part of ECI/SoGE climate community and
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Salary: Grade 6/7, £33,951 - £37,694 / £41,064 - £46,049 per annum This post is full time and has funding available for up to 8 months. We are seeking to appoint a Research Assistant / Associate to join our team in the School of Mathematics & Statistics. Working with Professor Surajit Ray, you...
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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the Department of Engineering Science at the University of Oxford. The post is funded by the Oxford Martin Programme on Circular Battery Economies. It is fixed term up to December 2027. You will undertake
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PolyU’s website at https://www.polyu.edu.hk/ . DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING Research Assistant Professor in Climate-Resilient Infrastructure or Large AI Models (Ref. 250811010