177 structural-engineering-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions at Monash University in Australia
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using structure‑based and medicinal chemistry approaches, contribute to pharmacokinetic optimisation, and support broader discovery activities, including data analysis, sample preparation, and report
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intelligence (XAI). This project will build on the methodology of formal explainable AI (FXAI) and aim at advancing FXAI technology and broadening its use by seeking (1) how to efficiently represent an AI system
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these methods to understand the atomic structure and properties of a range of materials being developed for efficient solar cells, light generation, batteries, superconductors, photonics and nanoelectronics. We
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Research Fellow - Organ-on-chip Technology Job No.: 692000 Location: Parkville Campus and Melbourne Centre for Nanofabrication in Clayton Employment Type: Full-time Duration: 2 year fixed-term
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of Pharmaceutical Science Advanced with Honours Bachelor of Pharmaceutical Science/Bachelor of Engineering (Honours) Benefits $6000 one-off payment. Number offered Ten scholarships available per year. Selection
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principles must the task choice be based for this to work? These questions are central to explaining the organisation of natural societies, from insects to humans, and to engineering self-organised systems
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Australia’s shift from single-use plastics to fibre-based alternatives. The centre aims to engineer fibre packaging as a sustainable fossil plastic substitute and optimise its recycling to foster a genuinely
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discover them The Opportunity The Department of Electrical and Computer Systems Engineering at Monash University is seeking a motivated Level A Research Fellow for a 2 year research-only appointment. A Level
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students across a range of subjects, including innovation management, new product development, technology commercialisation, and competitive strategy. You will be enthusiastic about applying diverse
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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and