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Medical School-affiliated hospitals. Responsibilities: Evaluation of multi-cancer early detection tests and implementation strategies in clinical and population settings Development and testing
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, climbing wall, cycle circuit, and sports halls. Personal Development: Access to courses run by our Organisational Development & Professional Learning team. Access to on-site childcare, shopping discounts and
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development fund of $2500 to spend on educational materials or conference attendance. MINIMUM QUALIFICATIONS: Applicants must have completed or be scheduled to complete an ACGME (American) or RCPSC (Canadian
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of the agency is to provide global leadership in agricultural discoveries through scientific excellence. Research Project: Research and development emphasis will be placed on cotton fiber science and new product
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The postholder’s primary responsibility is to conduct cutting-edge research on a new class of biochemical sensor development for wastewater, especially on the single-cell and single molecule detection, utilising
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Wastewater for Real-time Public Health. About the Role The postholder’s primary responsibility is to conduct cutting-edge research on a new class of biochemical sensor development for wastewater, especially
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. Funded by Innovate UK through a Knowledge Transfer Partnership (KTP), the Fellow will lead the development of an end-to-end AET testing framework, encompassing data assembly, software interfacing, test
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quantitative research methods, statistical analysis, and computational techniques; proficiency in programming languages such as Python, statistical software such as R, and basic AI/ML development Independent
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ecosystems to microbiology and developmental biology, from evolution to cell biology, from molecular biology to systems biology, bioinformatics, and genomics. It is always an exciting time for Biology
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Herston location About This Opportunity This is an exceptional opportunity for a Research Platform Fellow to shape the development of national infrastructure for AI Federated Learning through robust data