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technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word
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QTLomics as part of the project. Main responsibilities Collect and standardise functional information, including QTL data, RNA-seq, and Gene Ontology (GO) annotations Develop computational pipelines for QTL
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modelling or related quantitative methods. Experience with analysis of large, complex health data, such as longitudinal data, registry data, electronic health records, cohort data or trial data. Strong
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
sustainable rare‑earth nanocomposite materials for next‑generation energy storage applications, contribute to the activity cluster, School of Engineering, and the ANU College of Systems and Society. About the
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interdisciplinary research teams on quantitative analyses of complex genomic datasets; Learn to use remote, high powered computer clusters to process large datasets. Mentor: The mentor for this opportunity is Adam
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Applications are open throughout the year via the various links below. Applications received before 15 January 2026 will be considered for the 2026 intake. Interview by the respective clusters for shortlisted
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application of machine learning and AI methods to large-scale, longitudinal, routinely collected eRegistry data. The successful candidate will collaborate with researchers, PhD candidates, postdoctoral fellows
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 2 months ago
Position Details Position Information Position Title Donald Hill Family Postdoctoral Fellow - Artificial Intelligence in Health & Medicine Posting Number PTAP3278P Department/Unit Computer Science
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, metabolomics, proteomics) is an asset. Knowledge of data harmonization platforms (e.g., Maelstrom Research guidelines) is an asset. Experience with high-performance computing environments (Unix/HPC clusters) is
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of distribution networks with DERs, and applications of stochastic programming/approximate dynamic programming approach. Expertise in data analysis for clustering and classification Knowledge in visualization tools