41 computational-geometry PhD positions at University of Nottingham in United Kingdom
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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degree which includes a substantial research element, with a score of 65% or above in the taught modules and 65% or above in the dissertation. Complete the PhD Programme online application . Your
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Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after
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supervisors spans five departments at University of Nottingham including Architecture and Built Environment, Electrical and Electronic Engineering, Mathematics, Physics and Social Sciences. The PhD programme
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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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nationals only) and research costs) three-year full-time PhD available to start on the 1st October 2025. The overall theme of this PhD programme is improving clinical assessment and research access
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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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(School of Computer Science) External Partner: Build Test Solutions Ltd (BTS) Start Date: 1st October 2025 Eligibility: Home students only | Minimum 2:1 in a relevant discipline Stipend: Home students only
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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