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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 60% mark in
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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to apply: Online applications are made at http://www.bris.ac.uk/pg-howtoapply . Please select ICASE Trustworthy AI For Industrial Applications on the Programme Choice page. You will be prompted to enter
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the coordination guarantees. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Candidate
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of Sciences. We are a member of the Vienna Center for Quantum Science and Technology (VCQ), one of Europe’s largest hubs for quantum science, and of the Austrian Cluster of Excellence quantA, which supports
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of Sciences. We are a member of the Vienna Center for Quantum Science and Technology (VCQ), one of Europe’s largest hubs for quantum science, and of the Austrian Cluster of Excellence quantA, which supports
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Application deadline: All year round Research theme: Computational Chemistry, Material Science No. of positions: 1 Eligibility: UK students This 3.5-year project is fully funded by industry and home
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, surgery planning with patient data for surgeons, real-time remote guidance for maintenance in industrial plants, and iterative design simulation for architecture and engineering. However, its wide adoption
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degree or equivalent in a related discipline. This project would suit individuals with academic or industrial experience in electronics, electrical engineering, systems engineering, or AI/data analytics
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equivalent in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science/engineering, applied physics/mathematics, or related fields. Prior experience