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. Candidate Requirements A strong academic background in Engineering, Mathematics, Physics, Architecture or Computer Science. An undergraduate degree with at least 2.1 in one of the above subjects is essential
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A fully funded 3.5 years PhD position in developing software and computational tools for sustainable supramolecular materials design is available in the group of Assistant Professor Andrew Tarzia
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provide powerful tools to improve the quality and efficiency of data-driven models. In parallel to the development of data-driven models for dynamical systems with geometric structures such as Hamiltonian
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involve experimental optimisation, leveraging computational tools, statistical modelling, and emerging AI/ML applications to streamline and accelerate the workflow for complex mixtures and metabolomics
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing
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The probabilistic method is a powerful tool which has been especially influential in the fields of combinatorics and computer science. In the context of combinatorics, this method was pioneered by
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research. Specific multidisciplinary skills include: synthetic chemistry, structural molecular biology and advanced imaging; it will harness cutting edge computational tools (incl. image analyses), giving
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and programmable biomaterial synthesis. The ability to program the behaviour of biomolecular chemistry is foundational for developing new biotechnology applications. Redox-sensitive molecules are a
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is part of a wider programme of work led by Professor Michael Jenkinson and the Royal College of Surgeons for England (RCSEng) Collaborative. This collaborative includes Royal College of Surgeons