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Application deadline: All year round Research theme: Computational Chemistry, Material Science No. of positions: 1 Eligibility: UK students This 3.5-year project is fully funded by industry and home
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/ physics/ meteorology/ engineering/ biology. Experience in a computer programming language (e.g. Fortran, Python). A general understanding of the importance of the scientific method is essential. A previous
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exercise science, human nutrition, human physiology, biochemistry, food science or a related subject. A relevant Master’s degree and/or experience in one or more of the above subjects will be an advantage
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computational approaches. You will play a hands-on role in generating and analysing high-quality data that fuels discoveries, publications and therapeutic innovation. https://www.rdm.ox.ac.uk/research
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and will be jointly supervised by: Dr Dominik Leichtle, School of Informatics, University of Edinburgh Dr Elham Kashefi, School of Informatics, University of Edinburgh Dr Ivan Rungger, National Physics
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degree in Electrical Engineering, Biomedical Engineering, Mechanical Engineering, or a related field, with a genuine interest in Wearable Technology, Sensor Development, Biomedical Devices, and Data
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existing data analytics tools will help deploy these technologies in the industry context without the need for big datasets. Predictive Maintenance (PdM) is one of the maintenance strategies that has
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the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be
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suitable for a hard-working researcher with an interest in respiratory infections. Essential skills: A BSc degree or equivalent ideally in a health related field, excellent computer literacy, good inter
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This collaborative project between Jaguar Land Rover (JLR