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skills, plus good presentation and writing skills in English, are required. Previous research experience in contributing to a collaborative interdisciplinary research environment is highly desirable but
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strain-rate/high temperature interface contact layer created during LFW of Titanium alloys and the links to key process variables and machine/tooling behaviour. This study will be undertaken using
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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strain-rate/high temperature interface contact layer created during LFW of Titanium alloys and the links to key process variables and machine/tooling behaviour. This study will be undertaken using
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contribute to the development and validation of computationally efficient motor-twin models for permanent magnet synchronous machines. The work will focus on machine modelling, parameter and state estimation
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Start date – 1st March 2027 (latest) Project Description Machining generates large quantities of swarf, with many components losing 60–90% of their material during the machining stage. This swarf
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partnership with business, professions, the public services, the third sector and other research and learning providers. Applications are invited from potential doctoral students for the following social
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machine learning and conventional optimisation techniques. 2. To design and optimise magnonic primitives for wave-based neuromorphic computing, including programmable devices enabling nonlinear activation
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas
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management of juvenile fish habitats, the student will be trained in a range of inter-disciplinary skills including coastal fish sampling, scientific diving, digital technologies and computer vision. Would