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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science, applied mathematics, or a closely related field. Experience or interest in
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assurance practices. Prepares research summaries and assists in progress report development. Maintains laboratory work areas and supply inventory. Combinations of related education and experience may be
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a closely related field. A strong background in quantum mechanics, solid-state physics, and computational modeling. Previous experience with density functional theory or many-body physics (beneficial
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PhD fellowship in Experimental Quantum Physics PhD Project in Spin-Mechanics Niels Bohr Institute Faculty of Science University of Copenhagen The Niels Bohr Institute invites applicants for a PhD
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Swinburne University of Technology before commencing the scholarship. About You To be suitable for this role you will need to have experience in the below key accountabilities: Experience in modelling (CAD
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well as collaborate with others with effective written/oral communication skills. Knowledge of fluid mechanics or CFD is essential. Experience in programming (Fortran/C++/Python) and knowledge of acoustics and solid
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environments. As a PhD student, you will conduct experiments using state-of-the-art facilities such as the Diamond Light Source and the Surrey Ion Beam Centre, design and test irradiated stainless steel samples
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the cellular and genetic mechanisms that endow craniofacial muscles with specialized properties and resilience, particularly in the context of dystrophic and neurodegenerative diseases. Unlike trunk muscles
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- or nanoengineering, material science, mechanical engineering or similar. Experience with cleanroom fabrication processes or additive manufacturing is highly beneficial. You are expected to have a keen interest in
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across