408 parallel-computing-numerical-methods positions at University of Sheffield in United Kingdom
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desired transgenic flies. Analyse and record data using established methods and/or design new custom methods for data analysis. Set up and maintain lab equipment. Prepare papers for journals/presentations
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computational genomics methods such as the Mutability Adjusted Proportion of Singletons (MAPS). You will be expected, with guidance from the PI, to identify and adapt relevant methodologies, manage large data
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innovative teaching methods as appropriate. Supervise undergraduate and postgraduate students for individual projects/dissertations as required. Undertake module assessment across our undergraduate and
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numerous human activities, from fishing to climate change. Effective conservation and management of marine ecosystems requires that we understand the dynamics of marine biodiversity in both space and time
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Environmentswithin the EC Horizon programme. The project includes 21 partners in 5 countries coordinated by Tampere University. The UK team includes the University of Sheffield, Sheffield Hallam University and
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(written and verbal) and the ability to think and read critically. Essential Application/ Interview Highly numerate with the evidence of the ability to understand, interpret and communicate data and a good
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Directly Funded UK Students Dr Jon Willmott, Dr Matthew Hobbs Application Deadline: 23 June 2025 Details The fusion energy sector must develop methods of remote maintenance where human access is impossible
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net zero. https://greenindustrialfutures.site.hw.ac.uk/the-programme/training-programme/ Project: The automotive industry faces significant challenges in reducing CO2 emissions during the painting
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/or computational methods and/or experience of using APIs Desirable Application/interview Further Information Next steps in the recruitment process Details of the next steps will be provided
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine