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Field
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, including parsing and processing large document corpora. Strong understanding of machine learning or AI methods applied to health or biomedical data. Demonstrated ability to assess model outputs, identify
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analysis Machine learning and retrieval-augmented AI models for biomarker prioritization and decision support ·Work closely with cross-functional team members to develop hypotheses, interpret data, and
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graphs (ARGs). Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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learning approaches (Roux) to evaluate novel biomarkers for ADRD. Minimum Qualifications PhD in Computer Science, Engineering, Bioinformatics, Biomedical Data Science, or a related field Experience in
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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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Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine
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Fracture Network (DFN) and Embedded Discrete Fracture Modeling (EDFM) Tracer design and interpretation Machine learning or optimization for reservoir management Experience working with field-scale geothermal
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to
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aberration-corrected electron microscopy, pair distribution function as well as XRD refinement to demystify the microstructure-property of new composition-disordered thermal materials and apply machine