108 algorithm-"Multiple"-"U"-"Simons-Foundation"-"Prof"-"UNIS" Fellowship positions in United Kingdom
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will be based in the Environmental Lab at the University of Southampton, with opportunities to collaborate with leading researchers across multiple institutions within EBIC. The research will be
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end users Proficient in programming or scripting languages, including Python, C++ and Java Script Ability to work independently on multiple streams of research and collaboratively across multi
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administrative activities related to the DPhil. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. You will contribute ideas for new research projects, engage
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and Early Years. Engaging in knowledge dissemination and outreach through presenting at national and international conferences, publishing findings across multiple platforms, and contributing
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framework, mainly through the project’s techno-scientific stream: “Multi-hazard Risk Modelling, Quantification & Mitigation”. This will include 1) modelling physical impacts of multiple (including
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probe-based technologies for enzyme study We are one of the UK’s leading universities, internationally recognised for our expertise across a range of disciplines and research breakthroughs in multiple
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across a range of disciplines and research breakthroughs in multiple areas, including science, medicine and engineering, amongst many others. Our purpose is to transform lives, locally and globally, which
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quantitative techniques (e.g., NLP, classification, clustering), statistical modelling, and other computational techniques Process large scale text data sets in multiple languages Create documentation for data
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for X-ray equipment, a proven ability to maintain and repair the X-ray equipment and communicate with users. You will also have an ability to manage multiple priorities in a fast-paced environment
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partners. Main Duties Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species