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research excellence. To be successful, you’ll have: PhD in Education, Educational Psychology, Educational Measurement, Public Policy, Sociology, Economics, Statistics, Data Science or a closely related
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will have: A PhD (or equivalent experience) in medicinal or synthetic chemistry Strong hands‑on laboratory experience in synthetic organic or medicinal chemistry Excellent analytical, organisational, and
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work independently on technically demanding data tasks, with attention to reproducibility, documentation, and analytical robustness; proven experience supervising and mentoring others. Desirable (but not
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leading clinicians and researchers tackling real‑world challenges in critical care, data innovation, AI and advanced analytics. We are seeking a talented Data Scientist / Clinical Data Engineer
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
leading role in interdisciplinary research that links numerical modelling, geological and geophysical data, and mineral systems science. The Person The successful applicant will hold a PhD in Earth Sciences
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 19 days ago
to build a new approach to systems design and build new national capabilities in data-driven policy and business development. The work we do in the College directly supports one of the four pillars of ANU by
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structural biology, molecular genetics, ’omics technologies, synthetic and bioactive chemistry, materials and nanotechnology, supported by a strong, externally funded research portfolio and a vibrant PhD
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qualifications in related fields (e.g., neuroscience) experience in quantitative methods relevant to immune science experience in clinical research with strong data analytic skills with an interest in training in
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manage research datasets, including development of analytic workflows, REDCap data collection tools, algorithm development, and validation of NLP pipelines · lead the development of scholarly outputs
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machine learning, big-data analytics, and data-driven approaches to optimise composition–process–property relationships. Key responsibilities will include: Research: Conduct additive manufacturing research