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postdoctoral and PhD researchers on the team*Interest in developing and applying Large Language Models (LLM) and spatial Machine Learning (ML) modelsSalary and full employee benefits are offered in accordance
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: Appropriate PhD in related field Knowledge, Skills, and Abilities: Familiarity with appropriate laboratory and technical equipment; ability to effectively use a computer and applicable software to create data
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Expertise in molecular, cellular, or microbial biology. Experience with computational biology approaches, including analysis of large-scale sequencing data. Background and strong interest in evolutionary
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for FLASH-RT: strategies to obtain the shortest delivery time on healthy tissues and high dose-rate coverage of large volumes need to be evaluated for the current machine settings, high spatial and temporal
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 24 hours ago
/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and geographic data and work extrapolating results to different spatial scales
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and studying the phenological migration behavior of small insects using the terra-mobile platform on the Salter Research Farm. Analyze data and summarize results by writing scientific papers
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knowledge of statistical techniques for data analysis Experience in detector performance or trigger systems for high energy or nuclear physics experiments Experience with machine learning techniques and tools
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visual representation and analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division
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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