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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel the
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arc energy distribution becomes particularly complex in scenarios like parallel-pass deposition, thin-wall deposition, and off-centre or out-of-position deposition. Additionally, FEA models are focused
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel the
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, and more efficient hydrogen storage tanks. These materials will be designed to address current limitations in conventional storage systems, such as weight, safety, and energy density. In parallel
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. In parallel, the company is developing a geared engine targeting a 25% efficiency improvement over 2020 entry‑into‑service engines—an ambition that pushes thermal‑management requirements to the
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-performance computing. Key transferable skills include machine learning development, uncertainty quantification, collaborative research, technical communication, and the ability to translate AI
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discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in the Project element or equivalent with a minimum 60% overall
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Jennions Entry requirements A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum
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. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in the Project element or equivalent with a minimum 60% overall module average. the
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honours preferred), MSc or equivalent in Mechanical, Electrical Engineering, Control Engineering, Aerospace or Computer Science. Good mathematical background and experience with Matlab/Simulink, C