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, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
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learning experiences within real-world environments. Through strategic partnerships forged by our faculty with industry, learners bridge theoretical knowledge with practical application in and out
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from Theoretical Computer Science. Our research group is located jointly at the Faculty of Physics – as part of the Quantum Optics, Quantum Nanophysics, and Quantum Information Group – and at the Faculty
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scientific computing, to name a few. Modern LC applications rely heavily on accurate and efficient mathematical modelling of confined LC systems. Typical questions are - can we theoretically predict physically
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‘Campus’, please select ‘Loughborough’ and select the ‘Programme’ as ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number ‘FP-HZ-2025’ in your application under
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multidisciplinary academic team in the newly built Dalton Building with the state-of-the-art facilities equipped. Working with the industrial partner will hugely enhance the students’ capability to apply theoretical
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autonomous vehicles, aerial systems, and wider critical infrastructure, bridging the gap between theoretical advances and real-world operational needs. Students will benefit from close integration
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Develop practical, industry-transforming technology in this hands-on PhD program focused on immediate industrial applications. This exclusive opportunity places you directly at the interface between
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to increase each year. Tuition fees will also be paid. Home students are eligible. A funded PhD studentship is available in the field of computational inorganic chemistry. The project will involve prediction
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applicant must have (or be close to obtaining) a relevant PhD in Fluid Mechanics from an Engineering, Mathematics or Physics Department, a strong background in theoretical and computational fluid mechanics