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be awarded a master’s degree in any of the following disciplines: Chemical Engineering, Physics, Mechanical Engineering, Energy Engineering, Environmental Engineering, Computer Science, or other
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This PhD project aims to reduce—by approximately an order of magnitude—the time and computational effort required to evaluate point-defect energetics in technologically important mixed-anion
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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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, Continuum Mechanics, Computer Science, Materials Science, Physics or a related subject, or relevant industrial experience. English language requirements: Applicants must meet the minimum English language
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
relevant field such as engineering, computer science, or applied mathematics. Experience or interest in AI, machine learning, or digital systems is beneficial. We welcome candidates from diverse backgrounds
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of researchers who conduct cutting-edge research into NLP and AI within the University of Cambridge. Education: An excellent first degree in computer science, engineering or a closely related field Skills and
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select *Loughborough* and select the programme ‘CDT Engineering Hydrogen Net Zero’. Please quote the advertised reference number *LU-EnerHy-2025-3* under the ‘Finance’ section of the application. To avoid
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the harmful effects of deepfakes on ordinary people. You will draw on methods from psychology, linguistics, data science, and computer science, such as behavioural analytics, neuroimaging, face and voice
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background, we welcome applicants from other relevant disciplines, including psychology, sociology, criminology, law, and computer science. This PhD studentship is fully funded for up to 3.5 years with a tax
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vehicles. This offers a fully funded 4 years stipend and tuition fees at the Home status rate at University of Oxford under the supervision of Professor Matthew McGilvray & Dr Luke Doherty. Additionally