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models, frameworks, and methods to confront complexity underlying persistent societal challenges affecting the well-being of people and the planet. This position represents a unique opportunity to
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transcriptomic data, that will be integrated with clinical metadata and whole-genome data for developing machine learning models to identify and predict patient factors driving toxicity response and sensitivity
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. doi: 10.3389/fcell.2022.986261. Position Requirements Candidates must have (or soon receive) a PhD degree. Experience in mouse embryonic stem cell culturing and differentiation, genomic analysis
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Campus in North Austin. Purpose This position aims to advance innovative processing and acquisition methodologies for land DAS and dense surveys, focusing on synthetic modeling, 4D analysis, and seismic
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-performance computing, machine learning models (eg. LLM), probabilistic models for data, novel techniques for making measurements, visualization tools, and community-oriented foundational software tools. Please
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system to photometric data from NASA’s Kepler and TESS missions; rigorously test and determine the limits of model applicability, and publish the results in top-tier peer-reviewed journals (such as AAS
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Python and R for use in data science and modeling. Excellent writing and communication skills; demonstrated publication record. Preferred Qualifications Knowledge of cancer biology, clinical oncology
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Type: Salary Compensation Statement: The pay range listed for this position is determined by our compensation program using market data and salary benchmarking. A combination of factors is considered in
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position. Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: The compensation range described below is the
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Details Title U.S. Health Equity Postdoctoral Research Fellow School Harvard Medical School Department/Area Global Health and Social Medicine Position Description The Department of Global Health and