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                Research Fellow - Environmental Informatics Hub Job No.: 680160 Location: Clayton campus Employment Type: Full-time Duration: 2 year fixed-term appointment (with the possibility of an additional 2 
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                . We are currently seeking a Research Fellow with experience in AI and machine learning research and development, with a focus on any or all of following application areas: Computer vision Generative AI 
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                evidence-based care, advance post-ICU recovery science, and improve long-term patient outcomes. Your expertise in analytics, artificial intelligence, and data management will directly inform how critical 
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                for Health Economics is seeking a Level A Research Fellow to play a key role in an ongoing research program examining the effectiveness and cost-effectiveness of behavioural interventions, with a particular 
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                computation in science and engineering; Advanced materials and manufacturing; Energy and environment; Future cities; and Life sciences We are seeking an individual passionate about undertaking research in 
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                -designed health innovation Be part of transformative ideas shaping real-world health outcomes John the team at Turning Point as Research Fellow to play a central role in advancing a major program of 
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                research. Working closely with the Health Economics Program Lead, you will lead and support economic evaluations alongside clinical trials, including adaptive platform trials and novel trial designs, while 
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                the guidance of artificial intelligence techniques. The project will develop novel design processes that embed material behaviour within agent-based and machine learning computational design systems 
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                evaluate methods via experiments, benchmarking, simulation and/or real‑world data. The successful candidate will have: A PhD in Statistics, Data Science, Computer Science, Mathematics, or a related field 
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                . Your expertise includes machine learning techniques such as Bayesian optimisation, and you’re comfortable working with experimental data, high-performance computing environments, and (ideally) thin film