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are invited for a fully funded Industrial Doctoral Landscape Award in partnership with Siemens Digital Industry Software, focused on advancing the next generation of industrial Computational Fluid Dynamics (CFD
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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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, limited predictability and slow process optimisation. The PhD sits within an interdisciplinary research environment that combines laboratory experimentation with mechanistic and computational modelling
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awareness, and decision quality. The project will examine how system design, automation characteristics, and regulatory or governance constraints shape human performance and patient outcomes in safety
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Centre of Excellence. This is a unique opportunity to work on advanced image analysis and image-driven modelling as part of a wider multi-disciplinary programme that includes mathematical modelling, cancer
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two shafts while also allowing for assembly/disassembly. Building on a long history of work within the Transmissions UTC into the performance of spline couplings, this project will seek to further
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components which allow torque to be transmitted between two shafts while also allowing for assembly/disassembly. Building on a long history of work within the Transmissions UTC into the performance of spline
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the UK Atomic Energy Authority (UKAEA). The student will be based at the University of Nottingham, but should expect to engage fully with the 3-month full-time training programme in the Fusion Engineering
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engineering excellence needed for the aerospace sector. In this PhD, high-fidelity two-phase Computational Fluid Dynamics (CFD) methods will be used to model complex and fundamental cryogenic hydrogen flows
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and operations. Despite carefully designed and monitored timetables, disruptions occur unexpectedly, often triggered by infrastructure failures, human error, or external factors. Given the high level of