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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates
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. Preferred qualifications: D. in Quantitative Genetics/Genomics, Computational Biology, or Related Discipline. Skilled in single-cell transcriptomic analyses, machine learning and artificial intelligence
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to produce resilient and high-performing models. · PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record of applying ML in academic or industry
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to join our team. We invite individuals with expertise in microbiology, immunology, systems biology, or computational biology to contribute to innovative research on the female urogenital microbiome
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), data entry, and literature review. Ideal candidates will have a background in environmental chemistry or soil science and some lab experience. BACKGROUND CHECKS/CLEARANCES Employment with the University
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ASTRONOMY (BL-AST-IUBLA) Astronomy has a long history at Indiana University, beginning in 1856 with Daniel Kirkwood. The department was founded in 1895. Our doctoral program has produced over 100
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review or select your application. Knowledge/Skills/Abilities: Sufficient relevant experience to contribute significantly to a specific project. Experience with the use of computers and other appropriate
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biology, biochemistry, cell biology, immunology, or microbiology Knowledge of computers, including Microsoft Word, Excel and PowerPoint. Working Conditions Lab Environment. Application Documents Resume
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may then be reviewed in the order in which they were received until the position is filled. Early applications are encouraged. A criminal and employment history background check will be performed
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Biology, Biostatistics, Bioinformatics, Computer Science, Data Science, or related discipline. * At least 1 year of experience in bioinformatics research. * Quantitative training with good understanding