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leader is Associate Professor Soledad Gonzalo Cogno. About the project The successful candidate will contribute to the development of mathematical and computational models to enquire about the mechanisms
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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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interests in biomedical NLP and medical LLMs, multimodal and foundation models, ophthalmology and medical imaging AI, and real-world applications in biomedicine and healthcare. Please visit https
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, software engineering, computational systems, or related fields Develop novel methods, models, and technologies that address emerging challenges with respect to AI relevant to our sponsor base Communicate
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. Faculty are expected to teach students and model exemplary professional behavior through work with students, peers, administration, and community colleagues and to provide student support including
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Department: Medicine | School Biomed Sci - Biomedical Informatics We are seeking a highly innovative Research Scientist to lead the development of AI computational frameworks for modeling complex biological
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statistical tests to solve analytic problems, including using descriptive statistics, linear regression, logistic regression, survival analysis, mixed models w/repeated measures, multi-level modeling
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, incorporating new tools and models, updating laboratories, refreshing case studies, replacing obsolete technologies, recommending curriculum improvements to Faculty Course Directors. CMU Online Coordination
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, including the PBWT/GBWT family of haplotype algorithms for biobank-scale identity-by-descent (IBD) detection, the Med-BERT and CovRNN clinical foundation models for electronic health records, and the UDIP
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checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back