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of the University’s campuses. These reports disclose information including Clery crime statistics for the previous three calendar years and policies, procedures, and programs the University uses to keep students and
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completing a PhD in a subject relevant to the project, which includes (but is not restricted to) statistical physics, biological physics, soft condensed matter physics, and fluid mechanics. The post is funded
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academic programs at the UO. The Paul Olum postdoctoral scholar will have divided job duties between research and teaching. This position will pursue ongoing research in mathematics or statistics in
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) Application Deadline 5 Apr 2026 - 00:00 (UTC) Country Australia Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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Programming experience (e.g., R, Python) and interest in machine learning approaches Doctoral degree (PhD or equivalent) in Education, Psychology, or a closely related field by the start date Knowledge, Skills
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contact Hong Zhu, Professor, at hzhu2m@virginia.edu. Minimum Requirements: Doctoral degree Preferred Qualifications: PhD in biostatistics, statistics, or a related quantitative field by the appointment
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participation, etc. Qualifications Basic qualifications (required at time of application) Applicants must hold a PhD or must have completed all requirements for a PhD program (or equivalent) except
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Term: Initially 1 year, renewable. Appointment Start Date: As early as February 2026, but flexible Group or Departmental Website: https://med.stanford.edu/bridge-lab.html (link is external) How to Submit
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that incorporates a broad range of neutrino and dark-matter models, assessing their effects on large-scale-structure (LSS) statistics as measured by the power spectrum and bispectrum of galaxies or intensity maps
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Background in statistics and deep learning; Excellent programming skills in python; Familiarity with Unix command-line tools; Excellent communication and organizational skills; Ability to work independently