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implementation of 4D-Var variational data assimilation techniques using observational data, including ROMS 4D-Var or a comparable system. Experience with large-scale HPC workflows, collaborative scientific
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how clinical blood tests are used. The chosen candidate will develop novel theory, large-scale data pipelines, and collaborate with clinicians to drive impact from these models, and use them to benefit
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interdisciplinary research integrating large-scale population and clinical data with multi-omics, biology, and AI to investigate the causes and biomarkers of early-onset cancers and identify opportunities
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of electrochemistry and artificial intelligence. Ideal candidates will have experience in machine learning, large language models, AI-agent development and computational workflows, with particular interest in building
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), NGS library preparation and Illumina sequencing Experience with the analysis of large-scale NGS data (bioinformatics) Demonstrated proficiency in bioinformatic analyses in R, Python or similar
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). The successful candidate will be involved in projects to perform big-data research, bundling genetics, multi-omics, biomarker, clinical, and histopathological data, to harness the full range
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financial services. For more information about EPAR, please see: https://epar.evans.uw.edu/ This position is a combination of service and applied research and may include preparing confidential reports
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laboratory techniques • Thorough knowledge of population genetics/genomics, transcriptomics, and epigenetics • Experience with the analysis of large-scale NGS data (bioinformatics) • Demonstrated ability
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for analysis of health record data for patient diagnosis and outcome prediction. Perform large-scale querying and analysis of clinical health record databases. Engage with clinical collaborators, to place the
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modalities. The candidate will have the opportunity to work on “big data” studies in health and diseases, including Alzheimer’s disease and others such as schizophrenia, psychosis, autism, etc. We collaborate