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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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required. High level of computer literacy required. Preferred Qualifications In-depth understanding and hands-on experience in RNA-seq and ChIP-seq sample preparation, data collection, and data processing
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. The training program is designed to impart the skills necessary for submitting successful career development awards. The emphasis on translational clinical research will require competitive applicants
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including strongly correlated fermion materials, high-temperature superconductivity, topological electronic states of matter, developments and applications of computational methods at the density-functional
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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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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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to oversee research activities outlined in NSF Grant 2520154 “Understanding Expectation-Driven Learning in Early Childhood: An Experimental and Computational Investigation,” under the supervision of Dr. Kimele
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Experience · PhD in oceanography, atmospheric science, geophysics, economics, computer science, statistics, or a related discipline at time of appointment; · Familiarity with literature relevant to sea-level
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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