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the cutting edge of foundation models, spatial omics analytics, and computational pathology, with a mission to turn massive, heterogeneous datasets into actionable insights for precision immuno-oncology
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motivated researcher with a strong background in computational modeling, system identification, and uncertainty quantification for civil infrastructure. The successful candidate will join the Risk Assessment
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with statistical modeling (ideally Bayesian statistics) • Proficiency in Fortran, R, Python, Matlab, or ideally other common languages (e.g., C/C++) Strong computational skills Strong oral and written
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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of this research program—spanning basic mechanistic discoveries, application of high-throughput technologies, validation in cancer-relevant mouse models, and translation platforms—is to understand the fundamental
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, and in vivo models to elucidate mechanisms underlying tumor evolution and drug resistance. The candidate will have access to state-of-the-art technologies and work in a highly collaborative
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-employment physical including a drug screen. Additional Information: The focus of the NIH research program is on “Mechanotransduction and Mechanical Stress induced Postoperative ileus” (POI). The goal is to
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malignancies with a primary focus on lung cancer. The lab has established the use of multiple high-throughput screening technologies, genomics, proteomics, epigenetics, clinical samples, and animal models
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the maturation and degradation of eukaryotic mRNA and the rich variety of ncRNAs and the role of RNases in maintaining cellular homeostasis. Many of the lab’s studies use the model eukaryote budding yeast