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excellence. Assisting in the mentorship of junior analytical staff members and graduate students QUALIFICATIONS A PhD in computational biology, bioinformatics, statistics, computer science (Machine Learning
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Fellow to develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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technical depth in NLP methods and a clear interest in problems related to AI safety. Minimum Qualifications: PhD in Computer Science, Information Science, Computational Linguistics, Machine Learning, or a
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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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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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these activities. Other laboratory responsibilities, as they arise, under the direction of the PI. Required Qualifications* MD or PhD degree in biomedical sciences (or related field) with a minimum of 3 years
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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