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student, will work at the intersection of 3D scanning, point cloud processing, and industrial metrology to quantify as-built geometry, a task traditionally reliant on manual measurement and expert
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career stage. Experience with High-Performance Computing (HPC) clusters, geospatial foundation models, large-scale geospatial processing or cloud-based Earth Observation platforms would be
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appropriate to your career stage. Experience with High-Performance Computing (HPC) clusters, geospatial foundation models, large-scale geospatial processing or cloud-based Earth Observation platforms
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or Data Science graduate with strong practical computing skills and an interest in building usable software for real-world challenges. This opportunity is currently drafted as self-funded; no tuition
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mechanical engineering. Industrial and Societal Relevance • Acceleration of hydrogen technology adoption in clean transport by addressing key engineering barriers in storage and safety. • Contribution
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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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environmental protection and regulatory compliance while advancing the adoption of innovative treatment approaches This project offers a unique opportunity to work on an applied, industry-driven
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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