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. Position Summary The Institute for Fundamental Science at the University of Oregon is seeking qualified applicants for its open research pool. Positions are limited-duration, funding-contingent research
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intervention and data from diverse sources Proficiency in statistical and computational languages (SQL, SAAS, R, Python, etc.), skills Knowledge of both traditional statistical analysis (e.g., logistic
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
Posting Information Posting Details Department Carolina Institute for DD - 429401 Posting Open Date 04/14/2026 Application Deadline 04/18/2026 Open Until Filled No Position Type Postdoctoral Scholar
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. For program details and eligibility, please visit https://hr.gwu.edu/postdoc_benefits. II. POSITION INFORMATION Posting Number P010143 Job Open Date Job Close Date Part-time / Full-time PF Post Doc Full Time
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refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools for highly dynamic SatCom systems Implement and open-source proof-of-concept
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additions to your profile Peer-reviewed research outputs (e. g. publications, preprints under review, or substantial open-source contributions) in nature-, bio-, or cognition-inspired AI (e. g. neuro-inspired
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practices due to an applicant’s race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status. Minimum Qualifications Applicants should have a Ph.D. degree
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
Posting Information Posting Details Department School of Data Sci and Society - 397100 Posting Open Date 04/13/2026 Application Deadline 06/15/2026 Open Until Filled No Position Type Postdoctoral
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responsibilities. Maintains conformity with ethical standards in research. Maintains compliance with good laboratory practice including the maintenance of adequate research records. Engages in open and timely
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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available