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Field
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responsibilities will span all stages of research, including collecting data in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical analyses, running
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 26 days ago
of the most diverse health databases in history. Nutrition for Precision Health, powered by the All of Us Research Program is a new initiative to develop algorithms that predict individual responses to food and
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nervous system to date in an organism uniquely suited to combine this anatomical information with functional imaging, electrophysiology, quantitative behavioral assays, precise genetic manipulation
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record in either: next-generation sequencing (NGS) methods, biostatistics methodologies, genetic/molecular epidemiology, and/or bioinformatics or computational biology approaches/pipelines is required
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The University of Chicago Academic Jobs Instructional Professor (Open-Rank), Department of Computer Science Department of Computer Science, Physical Sciences Division Position Type: Other Academic
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, Databases, etc.), Machine Learning, Theoretical Computer Science (Discrete Mathematics, Algorithms, etc.). Experience with EdTech tools, such as Ed Discussion, Gradescope, GitHub Classroom, Canvas, etc
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Bioinformatics, Computational Biology, Computer Science, Biomedical Engineering, Computer Engineering, Genetics/Genomics or related field experience with ‘omics platform output experience with biological datasets
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Posting Details Position Information Title Part-Time Faculty, Dept. of Computer Science Rank Adjunct Instructor Tenure Information Non-Tenure Track Department Summary The Department
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of their class with respect to academic credentials. Qualification requirements: Applicants must hold a degree equivalent to a Norwegian doctoral degree in epidemiology, biostatistics, computational biology, or a
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Description Primary Duties & Responsibilities: Designs, develops, and implements: Algorithms and computer software for omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical