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anticipates appointment of a full-time, tenure-track Assistant Professor in Data Science beginning Fall 2027. A PhD in Statistics, Mathematics, Computer Science, or a closely related field is required by
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the Benefits Guidebook . Qualifications Required: A PhD in Data Science, AI, Statistics, Computer Science, Applied Mathematics, or related field. Candidates must have evidence of leadership in their field
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implementing novel data analysis and statistical methods for Type Ia supernova cosmology, and contributing to data acquisition and spectroscopic follow-up of upcoming large transient surveys (e.g. Rubin/ATLAS
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/apply.php?p=MTH-27-02 . JOB QUALIFICATIONS: Applicants with strong research potential in data science are encouraged to apply. A PhD in Statistics, Data Science, Mathematics, or Applied Mathematics is
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Max Planck Institute for Solar System Research, Göttingen | Gottingen, Niedersachsen | Germany | 7 days ago
qualification A PhD in physics or astrophysics (PhD must be awarded by the start date) Experience with statistical inference and/or modelling of stellar magnetic fields The ability to work in a team A good
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Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded
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be preferred. Requirements: PhD degree in Learning Sciences, Educational Technology, Human-Computer Interaction (HCI), Information Technology, AI, or a relevant field Demonstrated experience and
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11th October 2026 Languages English English English Faculty of Science and Technology PhD Fellow in Remote Sensing of Rain-On-Snow Preconditioning Sea Ice Melt Onset Apply for this job See
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delegates. The appointees are expected to submit applications for RGC grants, other local/overseas competitive grants and/or fellowship schemes. General Requirements Candidates should have (i) a PhD degree in
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-making under uncertainty relevant to security and defense settings. Research activities will focus on methodological innovation, theoretical development, and applied mathematical/statistical modeling. Why