77 parallel-and-distributed-computing Postdoctoral positions at Rutgers University in United States
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computational analysis of genome sequencing data in the Ellison laboratory. Position Status Full Time Posting Number 25FA1089 Posting Open Date 10/31/2025 Posting Close Date Qualifications Minimum Education and
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computational analysis of genome sequencing data in the Ellison laboratory. Position Status Full Time Posting Number 25FA1089 Posting Open Date 10/31/2025 Posting Close Date Qualifications Minimum Education and
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, on program implementation and outcomes, along with producing peer-reviewed publications and public reports. The Associate will also lead the coordination of statewide and local research and practice learning
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Posting Open Date Posting Close Date Qualifications Minimum Education and Experience The candidate should hold a PhD degree in Computer Science, Information Systems, Computer Engineering, or a related field
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. Posting Summary DIMACS, the Center for Discrete Mathematics and Theoretical Computer Science, invites applications for postdoctoral associate positions for 2026-2028. The postdoc will be mentored by a
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. Posting Summary DIMACS, the Center for Discrete Mathematics and Theoretical Computer Science, invites applications for postdoctoral associate positions for 2026-2028. The postdoc will be mentored by a
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Minimum Education and Experience Candidates shall have completed all requirements for a PhD in building sciences, engineering, architecture, urban planning, energy economics, public informatics, or a
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Effective spoken and written English required. High level of computer literacy required. The individual must be able to navigate the highly complex and often unpredictable nature of scientific research, as
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. The successful applicant will work in the areas of causal inference and statistical learning with high-dimensional observational data, including development of statistical and computational methods, and
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. The successful applicant will work in the areas of causal inference and statistical learning with high-dimensional observational data, including development of statistical and computational methods, and