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on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to advance the use of mathematical and statistical models
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health services to Princeton University faculty, staff, and employees. An integrated, evidence-informed model guides all UHS practices and services. UHS leverages clinical encounters and prevention efforts
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, such as survey and sampling design and data analysis (in R or Python), meta-analysis and/or document/text analysis, or computational modeling *An interest in mixed-methods approaches, including also
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specific experience in Large Language Models (LLMs), and Vision-Language Models (VLMs) Excellent programming skills (Python is required, C# and C++ is desired) Fluency in English Desired qualifications
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of the work builds on recent publications from the laboratory, e.g. integrating language models with mass spectrometry data (https://www.nature.com/articles/s42256-021-00407-x, https://www.nature.com/articles
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of interest include: Metabolomics, isotope tracing, metabolic flux analysis, quantitative modeling, mass spectrometry imaging, cancer metabolism, small molecule inhibitor discovery, dietary impact on cancer
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, molecular biology, microbiology, biochemistry, biophysics, computational modeling or related fields. Experience in metabolic engineering, synthetic biology, yeast genetics, or control theory is a plus but not