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research, publications and other research outputs. The purpose of this role is to assist with the data collection and analysis for this project, including research in archives based in the United States
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. Applicants must apply online at https://www.princeton.edu/acad-positions/position/36861 and include a cover letter, CV, and names and contact information for 3 references. This position is subject to the
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of concepts from quantum information to quantum materials; algorithmic aspects of quantum computing; topological aspects of magnetism; connections between condensed matter and high-energy physics
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based on selected hire's qualifications. Work Schedule/Location: Located in Dallas, Texas, this is an on-site role with a typical schedule of Monday – Friday 8am to 5pm. Cover Letter/Resume: A cover
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Regular Job Code 9546 Employee Class Add to My Favorite Jobs Email this Job About the Job The School of Public Health Division of Biostatistics and Health Data Science (BHDS) is seeking applications for up
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to their research. Ideal candidates will be expected to employ state-of-the-art computational tools to analyze phylogenetic, metagenomic and multi-omics data sets generated from different clinical trials. Applicants
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: https://www.princeton.edu/acad-positions/position/38801 and include a cover letter, CV, research statement (a discussion of past research, expertise, and research interests), and contact information
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. The selected candidate will assist with projects involving analysis of longitudinal data with missing, correlated data, and truncated outcome data. Pursuit of independent research that aligns with the project
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for the position, availability of funding, and satisfactory job performance. Salary Information: $50,000 Required Documents to Apply: Cover Letter/Letter of Application, Curriculum Vitae, Letters of Recommendation
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. without vegetation. Studying dust deposition and its impact on energy performance. Analyzing meteorological, soil, and energy production data. Contributing to the modeling of PV performance under local