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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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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 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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personalized biomechanical modeling research and development efforts. In this role, they will help develop and incorporate new model features into existing laboratory models and programs. They will support the
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such as spatial biology, single-cell RNAseq Multiplex, etc and animal (mice) models such as humanized mice, PDX models and other models simulating human patients. Responsibilities Designs and conducts
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assistants, and analyze the data they collect and prepare that data for publication and presentation at scientific meetings. This person will complete translation of an existing sheep model of chewing from a
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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