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for fuel system applications. While these methods provide a wealth of knowledge and information, they remain impractical for industrial use. Therefore, AI modelling techniques will be harnessed to develop
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to identify the material degradation and coatings applications details in extreme environments. A novel techniques/method will be developed with focus on better prediction and more accurate measurement of
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Electronics, will use computational simulations to study how thin films form during flowable chemical vapor deposition (FCVD), a process used to build advanced semiconductor devices. Unlike traditional CVD
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, support and thrive on the contributions of all of our employees and the communities they represent. We are proud to be an equal opportunities employer and encourage applications from individuals who can
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experimental error. Thus, if AI systems are supposed to perform research autonomously, they need to have error-reasoning ability. This fully funded PhD Studentship forms part of the Error-Reasoning Agents (ERAs
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is to develop a novel framework of autonomy in AI-driven science that can help address these important epistemological and ethical questions. The position forms part of the ERAs project, which aims
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have at least 2:1 degree in a relevant field. Applicants with the prior knowledge and experience in one or more of the following fields are encouraged to apply: computer programming (any programming
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Mechanics School of Mechanical, Aerospace and Civil Engineering, The University of Sheffield Email: p.ricco@sheffield.ac.uk Applications should be made at: PhD study | MAC | The University of Sheffield
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large, real-world datasets with ground truth labels, the application of data-driven approaches in the indirect damage identification context, while promising for network-level monitoring, has been largely
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ML. Funding and eligibility This fully funded, three-year full-time studentship is available to UK nationals only. The successful applicant will receive a generous tax-free annual stipend of £25,000