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suitable for a hard-working researcher with an interest in respiratory infections. Essential skills: A BSc degree or equivalent ideally in a health related field, excellent computer literacy, good inter
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that makes an impact. All that's needed is a place where ideas have the space to meet and grow. That's what we've created – and you are invited. Change starts here! The Faculty of Technology has a wide range
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EPSRC Doctoral Landscape Award: Embedded power electronics converters design for future electric vehicles Applicants are invited for a 4-year EPSRC Doctoral Landscape Award PhD programme, co
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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up in the Department of Psychology or in the School’s new Department of Methodology & Research Development, and at either our Edinburgh, Dubai or Malaysia campus. Applicants should get in touch with a
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discipline, complete an expression of interest (EOI). The steps are: Complete the EOI available at Next Generation Graduates Program (NGGP): Sports Data Science & AI - Centre for Data Science (qut.edu.au
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The Department of Chemistry invites applicants for a PhD fellowship in computational homogenous catalyst discovery. Start date is 1.9.2025 or as soon as possible thereafter. The project The project
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PhD Scholarship in Digital Mapping of Homemade & DIY Cultural Economies in First Nations Communities
Monash, the three-year interdisciplinary project brings together academics in media, creative industries, cultural studies, sociology, and human-centered computing. The successful applicant will join the
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
access to domain expertise, real-world data, and a pathway for industrial impact. A 3-month placement with the sponsor will provide practical experience in deploying digital twin technologies in aerospace
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, balancing efficiency and sustainability in AI deployment poses a significant challenge, calling for advances in model design and training to reduce environmental impact while maintaining high performance