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Field
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candidates with: • Relevant subject matter experience at required level (e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) • Willingness to adapt and work across different
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problem that affects consumer trust, regulatory compliance, and public health. Often targeting high-value commodities, advanced analytical methods are needed to monitor the integrity of the supply chain and
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an exceptional opportunity to conduct cutting-edge research at the intersection of machine learning, healthcare, and computational modelling, contributing to real-world clinical impact. What is offered
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an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement
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(e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) Willingness to adapt and work across different disciplines Ability to work independently and cooperatively Commitment
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
of interest, skills and previous experience, motivation for Doctoral Research, future goals, and why you are applying to this project. On the application system select Programme of Study – PhD
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of developing a computational model to simulate hydro-sedimentary dynamics and morphology between restored patches to explore conditions favourable for restoration. Validated with data on vegetation cover and
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based in School of Computing and Creative Technologies at The University of the West of England , Bristol where they will join the Music Interaction and Computational Arts (MICA) Lab . The MICA Lab is
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background, we welcome applicants from other relevant disciplines, including psychology, sociology, criminology, law, and computer science. This PhD studentship is fully funded for up to 3.5 years with a tax
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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students