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Postdoctoral Research Scholar in Machine Learning and Computational Genomics Department of Epidemiology, School of Public Health, University of Pittsburgh The Department of Epidemiology
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that currently lack effective treatments, such as Parkinsons Disease. By combining machine learning with quantum chemistry and structure based approaches, the project will accelerate the translation
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constitutive androstane receptor (CAR) as models. PXR and CAR transcriptionally regulate cytochrome P450 3A4 (CYP3A4) and CYP3A5-drug-metabolizing enzymes that metabolize more than 50% of clinical drugs
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targets to treat anhedonia. Proposals that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects
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, biochemical, cell, and tissue biology method skills. Experience in using computational analysis (biostatistics, machine learning, data science, physics, or a related field). We value diversity and strongly
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of the following methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages
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think outside the box, to learn fast, collaborate effectively, iterate quickly, and work at the interface of both experimental and computational design. Qualifications for Computer Scientists, AI/ML: PhD
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array
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, or organoid co-culture systems Computational/bioinformatics skills (e.g., R, Python, machine learning, or similar) are a strong plus. Salary and benefits Salary will follow the University of Pennsylvania FY26
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex