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to collaborators as well as broader audiences through e.g. conference presentations. Candidates must hold a PhD degree in supramolecular chemistry, and will have experience in the synthesis, modification, analysis
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approaches, i.e. machine learning. Experience and knowledge in sorbent-based CO2 capture. Experience of interaction with industry. Experience supervising students. Skills in writing bids for research grants
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with PhD students and the other PDRAs of the Prosperity Partnership. The PDRA will report on project progress and outcomes to the Prosperity Partnership Management Group, as well as participating in
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applied statistics and statistical machine learning within healthcare. The successful candidate will have the opportunity to shape the curriculum, drive impactful research, and contribute to public health
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publications, for stage in career, and evidence of contribution to the writing of these publications proportionate to opportunity. Experience with machine learning and/or machine vision applied to research in a
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, perception, serious games, simulation, AI, Machine Learning, sensors, decision-making, and to communicate this expertise to the Transition Engineering Lab colleagues, and employ the cutting edge knowledge in
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(CVN ), Prof. Pock from the Institute of Computer Graphics and Vision at Graz University of Technology (ICG ), Prof. Thiran from the EPFL Signal Processing Laboratories (LTS5 ), Mr Jackson at the UoE
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, the Leonardo engineers involved in the project, and Dr Sam Tammas-Williams and Prof Jonathan Corney from University of Edinburgh. They will also work with PhD students and the other PDRAs of the Prosperity