147 parallel-and-distributed-computing-"U"-"Washington-University-in-St" positions at University of Nottingham in United Kingdom
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Technology, The University of Nottingham. Applicants are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in on-platform manufacturing engineering. The
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About the role The role-holder will work dynamically within the Rights Lab’s Business and Economies programme on externally-funded research projects that focus on analysing human rights and modern
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We are now recruiting 2 full time Research Fellows to work with Stephen Farrall and colleagues at Warwick (Emily Gray) and Queen Mary College, University of London (Maria Grasso) on their research into politics and crime. The research is funded by the Leverhulme Trust. The School and the...
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting-edge advancements in automated drug discovery through the integration of high data-density...
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PhD Studentship: Revolutionising Litz Wire Development for Next Generation Ultra-High Speed Propulsion Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers an exciting opportunity to undertake industrially linked research with leading engineers at the...
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Rolls-Royce University Technology Centre (UTC) in Manufacturing and On-Wing Technology Applicants are invited to undertake a fully funded three-year PhD programme in partnership with industry
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for career progression. Employee Assistance Programme and Counselling Service- 24/7 support. Supplier discounts, travel, and reward schemes. Staff Networks, and wellbeing activities and state of the art sports
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the results of this project. Candidates must possess a good first Degree (or Master's) and PhD (or near competition) in Engineering, Mathematics, Physics, Computer Science, or related disciplines. Your working
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project which aims to understand which computational (reinforcement learning) mechanisms are engaged by different antidepressant treatments and through this improve targeting of future treatments for
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to understand which computational (reinforcement learning) mechanisms are engaged by different antidepressant treatments and through this improve targeting of future treatments for clinical depression