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Field
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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managing human subjects research and handling sensitive data is preferred. • Strong quantitative skills, including proficiency in regression modeling, environmental mixtures analysis, and spatial methods
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working with HPV-positive cell lines is a plus. Preferred Schedule: 7am to 5pm Position Requirements: Minimum Qualifications: Minimum education: PhD in a scientific field or MD Minimum experience: 0-3 years
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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School of Medicine Benefits Eligible Postdoctoral Fellows may be eligible to receive certain UNM benefits . See the Benefits home page for more information. Position Summary The Department
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
Posting Information Posting Details Department Microbiology and Immunology - 422501 Posting Open Date 08/04/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar
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to incorporate these methods into models based on systematic testing and validation relative to current and historical data. This position will work closely with Virginia’s Land Use-Value Assessment Program
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required Working knowledge of all aspects of planning and deploying an AI project, including initial problem specification, data gathering and analysis, model creation, and implementation Strong
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upon a storm surge model for the southwest Florida coast to evaluate coastal flooding resulting from recent hurricanes. Please submit a cover letter, curriculum vitae, and contact information for three
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extensibility. The ideal candidate for this position will possess: A strong background in scientific programing Interest in developing open-source data analysis and modeling tools in one of the following