36 parallel-computing-numerical-methods-"Simons-Foundation" PhD positions at University of Nottingham
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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which
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). This studentship will include a placement at Astra Zeneca, Cambridge and is part of a broader Medical Research Council Programme grant focused to understand mucus regulation in severe asthma. The project will
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Applications are invited to undertake a PhD programme, in partnership with Airbus, to address key challenges in ensuring adoption of sustainable approaches to fuel additives for aviation use
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approx. £15-17k across full PhD programme). Monthly stipend based on £20,780 per annum, pro rata, tax free. Working hours: Full-time (minimum 37.5 hrs per week). Working style: Primarily in-person at host
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27 Sep 2025 Job Information Organisation/Company University of Nottingham Research Field Computer science » Other Engineering » Biomedical engineering Medical sciences » Other Researcher Profile
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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individual with a 1st or a 2:1 degree from Mechanical, Manufacturing, Mechatronics Engineering, Computer Science or other relevant field. The candidate should have excellent analytical and communications
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training and mentoring programme in place that consists of both key skills training and online monitoring of research progress. Project Options: Option 1 - A multi-omics spatial approach to characterise and
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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device