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experiments and cognitive modelling. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience
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grade. Generate data through comprehensive laboratory grinding tests on various rail grades to train and validate the ML model. Utilise numerical modelling to establish acceptable thresholds for surface
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models? How can machine learning and computer vision models be adapted to accurately classify the different tool wear mechanisms (like abrasion, adhesion, diffusion, and fracture) from high-resolution
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of the UK and Northwest Europe's dynamic patterns of atmospheric circulation processes and how they are affected by climate change. We will develop, adapt and use data-driven modelling techniques, including
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'lifeing model' to optimise service life based on surface integrity. If you are a motivated student ready to harness experimental data and digital modelling to improve critical aerospace components
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reporting Work with and support other members of the team, including in the supervision and support of PhD students and MSc students working in the lab Engage in the Readable Research Lay Summary Scheme as a
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. Information on what documents are required and a link to the application form can be found here - https://www.sheffield.ac.uk/postgraduate/phd/apply/applying The form has comprehensive instructions for you to
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for the Research Associate, Grade 7 level, position must have a PhD in a quantitative biology discipline, statistics or machine learning along with a proven track record of research using statistical modelling
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have expertise in data analysis, microeconomic modeling, and relevant econometric techniques. Excellent communication skills and the ability to work both independently and as part of a team are also
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Overview A research associate position is available within an exciting project aiming to conduct research into the safety and security of advanced hardware architectures. The project is called “Co