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bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms Downloading a copy of our Job Description Full
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COG-MHEAR is a world-leading cross-disciplinary research programme funded under the EPSRC Transformative Healthcare Technologies 2050 Call. The programme aims to develop truly personalized
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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Research Fellow in Intervention Development to join the Big Data in Health Group About us Our big data in health team at the University of Southampton is based in the Primary Care Research Centre
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real-time, and will be tested and demonstrated on a state-of-the-art HiL rig and an autonomous test vehicle. The post is focused on the development of automotive-grade algorithms and estimators that will
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John Williamson, and Dr Sebastian Stein. The job requires the proven ability to develop novel theory and build and evaluate working interactive prototypes involving complex computational models
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inversion techniques and signal processing. Strong programming skills, Proficiency in scientific computing (e.g. Python, MATLAB, or similar) for algorithm development and data handling. Experience with sensor
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, the appointed candidate will work closely with the line manager to develop novel control algorithms in EAP soft robotics combining Gaussian Predictors, hands-on laboratory experiments and JULIA computing
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Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims