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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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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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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