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engineering, physics and applied mathematics. You should have experience in one or more of the following: numerical methods, high-performance computing (HPC), Computational Fluid Dynamics (CFD), applied
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, adaptability, and sustainability from the outset. By scientifically linking high-level performance objectives to system architecture and design decisions, this research aims to reduce costly late-stage redesign
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The project: The High Performance Computing (HPC) services of today provide the computational density to perform groundbreaking science. These services facilitate the distribution of a problem space
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Electrophysical remanufacturing of aerospace gas turbine components for performance restoration and critical material safeguarding This exciting opportunity is based within the Advanced
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learning, control theory, and embodied autonomous systems. The successful candidate will contribute to the development of learning-based control methods that are not only high-performing, but also safe
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responsibilities and duties include: Designing research framework and methodology and performing operational and functional testing of the experimental system to a high standard. Building positive relationships with
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PhD studentship in Computer Science - Dynamic Validation of AI Systems in Digital Twins: A Real-Time Safety Framework for Critical Infrastructure Resilience Award Summary 100% fees covered, and a
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The project: The domain of High Performance Computing (HPC) effectively utilises massively multicore computers that facilitate the distribution of scientific workloads across thousands of compute
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to assess feasibility and optimise performance under uncertain subsurface conditions. Two principal configurations are employed: closed-loop systems, commonly referred to as deep borehole heat exchangers
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Materials, mastering scientific machine learning, uncertainty quantification, and high-performance computing. Your models will inform fusion design and advance AI-for-materials. Perfect for physics, maths