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questions as to the short and long-term impacts on vascular disease risk. Published work from our laboratory has shown that both subcutaneous infusion of the major tobacco component, nicotine, and inhaled e
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learning experts will be an essential and enriching component of the position. Strong candidates will have a background in machine learning and natural language processing (NLP), with a demonstrated ability
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. Required Qualifications: A doctoral degree (PhD, MD, or equivalent) conferred by the start date. Proficiency in R/Python Experience with scRNAseq, and/or spatial proteomic/transcriptomic data analysis Growth
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& Brian Hargreaves. Partial list of applicable skills: Expertise in MRI physics Experience with raw MRI data management Experience with MRI reconstruction Clinical studies: data collection / analysis Pulse
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given to candidates studying early China using analytical methods such as zooarchaeology, paleobotany, ceramic analysis, and lithic analysis. The successful candidate will be expected to: Teach one course
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, Tumor Immunology, Molecular Biology, Protein Biology, Mouse work, Flow Cytometry/FACS, LC-MS/MS, TCR- and RNA-seq, and AI analysis. Required Application Materials: CV, research summary, and published
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tumor spatial biology via single cell spatial analysis of the tumor microenvironment. Projects aim to reveal molecular mechanisms of drug resistance based on intercellular and intracellular regulation
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protocols. Provide hands-on technical/operational support for preclinical FLASH irradiation experiments of multiple users in collaborating laboratories Contribute to data analysis, manuscript preparation, and
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survival data using longitudinal features, and (6) machine learning and deep learning for analyzing time-to-event outcomes, or (7) radiomics and medical imaging analysis. Required Qualifications: We seek
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and multi-omics data analysis, and a high-performance computing environment (Unix/Linux) is highly preferred. An individual with Next generation sequencing experience is preferred. A good understanding