128 parallel-computing-numerical-methods-"Simons-Foundation" positions at King's College London in United Kingdom
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effectively across competing priorities and work to tight deadlines Excellent attention to detail with methodical, exacting standards of written and numerical accuracy Excellent IT experience across all
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London, acting as the main contact for programme queries and providing excellent customer service to prospective applicants, parents, and students. They will play a key role in assisting the Programme
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support processes within our main workstreams of Assessment and Award, Programme Planning and Delivery and Student Communications and Knowledge ensuring accurate and timely record management across multiple
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About us The Department of Digital Humanities is a global leader in researching digital culture and society, and in exploring the use of advanced technology-related methods in humanities research
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future development of an ongoing epilepsy research project funded by the UK Epilepsy Research Institute. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on
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ability to analyse complex data, with proficiency in Microsoft packages (Word, Excel, Teams, PowerPoint, and Outlook). Methodical, detail oriented and flexible in approach to work, maintaining a high level
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documentation and compliance. They will work closely with clinical teams to identify eligible participants and ensure high-quality engagement with trial protocols. In parallel, the fellow will contribute
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2000 staff and holds an Athena SWAN Silver award. It has a comprehensive programme of initiatives to support an inclusive environment and culture, and to promote diversity across our staff and student
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for molecular simulations Track record of method development for molecular simulations Experience of working effectively in a team Experience of working with high-performance computing * Please note that this is
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PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets Experience in statistical or scientific programming (ideally R and/or