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, CUDA, etc. Experience with numerical methods such as FDTD, FEM, BEM, etc. Basic knowledge of numerical linear algebra concepts, such as matrix factorization and decomposition algorithms. Familiarity with
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
possess sufficient breadth or depth of knowledge in the discipline and of research methods and techniques to work within own area. The candidate will also have the ability to continually update knowledge
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present with neurodevelopmental deficits associated with Autism Spectrum Disorder and hyperphagia, and in healthy controls. We will be using a range of methods, including behavioural phenotyping, cognitive
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a thorough knowledge of mechatronics engineering for electric vehicle systems and to have a working knowledge of a range of topics within the sphere of their chosen discipline. They will also be
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complex input. For instance, in physics-informed ML, in addition to data examples used by a standard ML setup, domain knowledge serves as an additional input. It can be in an explicit form of rigorous
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considered. The ideal applicant will have a strong interest in cyber security and cryptography, and will have an interest in interdisciplinary research. Knowledge of mathematics to the equivalent of first-year
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knowledge of laser diagnostics for flames, experience with combustion experiments and in particular hydrogen and liquid fuels, image processing, and excellent knowledge of turbulent combustion. Appointment
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, reports and papers, and interact with project partners. The skills, qualifications and experience required to perform the role are very good knowledge of laser diagnostics for flames, experience with
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the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast