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Functionally Graded Materials (FGM): New Configurations and Designs School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application Deadline
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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delivering an open, sustainable simulation tool for the research and engineering community. Key Responsibilities: Development of AI-augmented CFD frameworks Simulation of particle-laden two-phase flows Valid
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
system health monitoring, and more efficient maintenance planning. Digital twins offer a powerful foundation but must evolve beyond simulation to truly support engineering decisions. This PhD will develop
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. Qualifications: The PhD research fellow must hold a master's degree in computer science, medical technology, applied mathematics, or in a closely related field, with a strong emphasis on computing. A solid
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collaboration with our clinical colleagues for investigating visual motion in a group of healthy subjects. The successful applicant holds a PhD degree (or equivalent) in a relevant academic area such as applied
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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Completion or near completion of a PhD in Engineering (mechanical, aerospace, civil, petroleum) or Science (applied physics, applied mathematics) with a strong background in CFD and the finite element method
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equivalent) in Civil Engineering, Environmental Engineering, Hydrogeology, Geosciences, Environmental Sciences, or related STEM disciplines (e.g., Applied Mathematics, Physics, Computational Sciences
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have