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Research theme: Formal Methods How many positions: 1 This 3.5 year PhD is funded by the Department of Computer Science at The University of Manchester. The successful candidate will receive
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, and public engagement. The programme is designed to prepare graduates for careers in academia, industry, or clinical innovation. Key training features include enrolment in a leading UK doctoral
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or sensor arrays. Experience generating, processing and analysing large material property datasets including correlating between multiple techniques, or developing computational reconstruction techniques
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or sensor arrays. Experience generating, processing and analysing large material property datasets including correlating between multiple techniques, or developing computational reconstruction techniques
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, showing potential to treat various cancers. The aim of this project is to use a multidisciplinary approach involving (bio)chemistry, computer aided drug design, cell biology and genetics to develop more
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computational mechanics or multiphysics modeling, with particular interest in fracture mechanics and chemo-mechanical degradation. Knowledge of solid-state defect chemistry (advantageous). You will join a dynamic
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funded PhD position is available to evaluate variation in spinal treatments using engineering methods. Specifically, the project will develop and validate novel computational testing methods to evaluate
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Engineering, Physics or Materials Science Excellent English written and spoken communication skills The following skills are also highly desirable: Ability to program in Matlab, Python or similar Strong
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degree in statistics, computer science, data science or computational biology. Ideally, the applicant would have a background in both biology/biomedical sciences and statistics/data science. Applicants
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for sustainable aerospace applications. By leveraging non-equilibrium reactive plasmas, PAC can modify flame chemistry, reduce emissions, and enable the use of alternative fuels in propulsion systems