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used by a PhD student, likely starting October 2027, to initiate development of a deep-learning architecture. The refined batch of synthetic data will be used by the PhD student to finalise the deep
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concordance and adverse health outcomes using large -scale healthcare datasets. Working collaboratively with clinicians, researchers, patient partners and data specialists to ensure scientific quality and
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. Under the supervision of Luke Johnson, PhD and Jerrold Vitek, MD PhD, the associate will work collaboratively within the NMRC on an existing project supporting the collection and analysis of large-scale
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protocols, and test hypotheses and analyse scientific data. You will be expected to contribute ideas for new research projects, develop ideas for generating research income, present detailed research
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successful in this role, you will hold (or be close to completing) a PhD/DPhil in machine learning, artificial intelligence, computer science, epidemiology, health data science, or a related quantitative
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consortium connects AI model development directly to large-scale microbiome data generation, microbial culture collections, greenhouse experiments and field validation. More information For more information
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applicable. Preferred Qualifications PhD in data science, and/or public health or related fields including health services research, health informatics, computer science. Experience in data analysis using
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ice, using a framework consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ
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of research investigating cancer risk and prevention. The appointee will work closely with Professor Ruth Travis, Dr Karl Smith-Byrne and other members of the research team, using large-scale epidemiological
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and