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the world's most pressing challenges. The PIIRS Postdoctoral Fellows Program is integral to that mission. We will award up to four postdoctoral fellowships to our 2027-28 cohort. PIIRS seeks recent PhDs in
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. Qualifications Required Qualifications PhD in Computer Science, Materials Science, Physics, Chemistry, Geosciences, or a related field. Strong background in machine learning, scientific computing, or computational
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to that mission. We will award up to four postdoctoral fellowships to our 2027-28 cohort. PIIRS seeks recent PhDs in the Social Sciences who have demonstrated exceptional scholarship, congruent with the Institute's
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of Engineering and Applied Science invites applications for postdoctoral and more senior research positions. Individuals with evidence of experience in scholarly research and a strong commitment to excellence in
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position; salaries for part-time positions are pro-rated accordingly. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information. Requisition
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reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly. The University also offers a comprehensive benefits program to eligible employees. Please see this link
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, computer science, and genetics. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding
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for the Postdoctoral Research Associate role . DDSS supports technical and methodological innovation in quantitative and computational social science, addressing a diverse array of new data and analytic challenges
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and collaborative research on the above topics. Preference will be given to candidates that have obtained their PhD within the last year. While this listing is open to all social science disciplines
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Department invites applications for postdoctoral and more senior research positions in two areas of computer science. 1) Quantum computing and quantum architecture and systems. 2) Reinforcement learning