82 phd-mathematical-modelling-population-modelling Postdoctoral positions at Stanford University
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model fairness and model generalizability across multi-institutional electronic health records databases. The researcher will have access to the real-world EHR data from almost 20 sites across
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, molecular biology, and in vivo models. Analyze and interpret data, integrating experimental and computational findings. Utilize bioinformatics tools and techniques to analyze high-throughput sequencing data
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large-language model applications in healthcare systems, systematically identifying ineffective clinical processes, bioinformatics analyses of population health, as well as more conventional outcomes
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Posted on Mon, 08/04/2025 - 17:10 Important Info Deprecated / Faculty Sponsor (Last, First Name): Knowles, Juliet Other Mentor(s) if Applicable: Frank Longo, MD PhD Stanford Departments and Centers
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success and publication history, with an MD, PhD or MD/PhD degrees, and very strong references. We are seeking a candidate with expertise in immunology. Previous experience in cancer research, molecular
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position will require establishing new model species in the lab, developing protocols for experiments that have not been attempted before, and collaborating with an international and interdisciplinary team
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. The position is fully funded. Required Qualifications: The successful candidate should be highly motivated and hard working, with outstanding past research success and publication history, with an MD, PhD or MD
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. Qualifications for this position include a PhD in Computer Science, Artificial Intelligence, Natural Language Processing, Human-Computer Interaction, or a closely related field. Candidates should have demonstrated
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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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language processing (NLP) to augment clinical decision-making and expand access to high-quality healthcare. Our lab develops new methods to improve model trustworthiness and leverages heterogeneous clinical data