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management High-performance computing and large-scale data analysis Innovation, entrepreneurship, and technology transfer Scientific communication and transferable skills Training activities will be organised
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developing the first observation-driven, AI-ready global river data infrastructure. Reporting to Professor Louise Slater, you will lead research combining large-scale Earth observation data, global hydrography
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venues and workshops) together with the ability to design, run, and make sense of large-scale experiments on foundation models. Informal enquiries may be addressed to Fazl Barez at [email protected]
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at large. The University of Edinburgh is a world-class organisation. We look for the best in the field across all disciplines and provide a working environment where academics can develop their careers and
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the causes, prevention and treatment of disease. We are looking for a researcher to lead genome-wide association studies of wearable-derived phenotypes across large-scale population biobanks. You will
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whole exome/genome sequence data in both clinical genetic studies and/or large-scale population studies, such as the UK Biobank Study. Applicants must have a degree in genetic epidemiology, statistical
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resilient infrastructure. The successful candidate will join a large dynamic team of researchers working on different challenges. The key responsibilities are driving, planning and conducting innovative
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processes for manufacturing large area meta-materials. In particular, we will focus on roll-to-roll (R2R) processing methods, which we seek to combine with emerging bottom self-assembly processes and top-down
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green finance and regional development. Key Responsibilities for the role include: Data collection, cleaning, and merging from large-scale microdata sources (e.g., patents, dissertations). Developing new
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holder will be located in Central Cambridge, Cambridgeshire, UK. The role involves: Designing and carrying out quantitative single-cell infection experiments, Analysing large microscopy datasets Developing