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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
Posting Information Posting Details Department Earth Marine Environmt Sci - 318000 Posting Open Date 08/11/2025 Application Deadline 09/08/2025 Open Until Filled No Position Type Postdoctoral
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The Princeton University WET LAB (https://ren.princeton.edu/) is seeking a postdoctoral research associate(s) or more senior researcher(s) with expertise and interest in Large Language Models (LLM
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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with collecting and analyzing data gathered from human subjects, both in field, clinic and lab studies as part of evaluations of the technology. A large part of the role will focus on supporting a
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experience, will work as part of a larger team to assist with collecting and analyzing data gathered from human subjects, both in field, clinic and lab studies as part of evaluations of the technology. A large
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, including large-scale data analysis, alpha signal research, and strategy performance enhancement. While there is some overlap with the Quantitative Systematic Trader role, Quantitative Researchers typically
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spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available starting July 2025, and will remain open until excellent fits are found.The
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fruit quality. Bioinformatics analysis of genomic features and genetic diversity in tropical fruit germplasm collections. Handling large genomics datasets. Preparation of data for scientific reports and
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of Public and International Affairs. The research position requires a PhD in a relevant field (e.g., geography, urban planning, data science, sociology, public health, emergency management). Ideal
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questions in human disease and health. Our work focuses on Alzheimer’s disease, cancer, and heart disease, which could have a major impact in identifying real-world big data-driven diagnostic biomarkers and