151 postdoc-parallel-computing positions at University of Nottingham in United Kingdom
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propulsion. Who we are looking for We are looking for enthusiastic, self-motivated applicants with first-class degree in electrical engineering, Aerospace Engineering or Computer Science with good electrical
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the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be
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for An enthusiastic, self-motivated individual with an interest in empirical and modelling work to test out new reactor designs. This will involve some work with Matlab or similar program to quantify mixing systems
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leadership contribution to the running of the BMBS programme providing academic oversight of the Advanced Clinical Practice component of the curriculum. This is a 0.2 FTE role which can be undertaken flexibly
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your skills and gain industry recognised qualifications. Personal and Career Development support, and opportunities for career progression. Employee Assistance Programme and Counselling Service- 24/7
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recognised qualifications. Personal and Career Development support, and opportunities for career progression. Employee Assistance Programme and Counselling Service- 24/7 support. Supplier discounts, travel
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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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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in
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programme to help you progress in your career. The post is offered on a full time (36.25 hours per week), fixed term contract until 31 May 2026. Applications are also welcome from candidates wishing to work
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’ or ‘internationally excellent’. The highly research active SP Section comprises 13 permanent academic staff with research interests in Bayesian computational statistics and machine learning, uncertainty quantification