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Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). We especially value candidates with strong technical skills for data-intensive research and statistical modeling. IU Astronomy has
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for LLMs. Experience with time-series data, anomaly detection, or predictive maintenance is a strong plus. Familiarity with industrial datasets and domain-specific challenges is desirable. Excellent problem
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architectures, attention mechanisms, and fine tuning techniques for LLMs. Experience with time-series data, anomaly detection, or predictive maintenance is a strong plus. Familiarity with industrial datasets and
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one or more of the following topics: Computationally intensive methods in Statistics such as Count Time Series, Curve estimation, Environmental Statistics, Forensic Statistics, Stochastic Modelling
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 24-Aug-25 Location: Riverside, California Type: Full-time Categories: Academic/Faculty Internal Number: JPF02131
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9 Oct 2025 Job Information Organisation/Company University of Beira Interior Research Field Engineering » Electrical engineering Engineering » Computer engineering Researcher Profile First Stage
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NSF-Simons Foundation-funded National AI Institute for the Sky | Chicago, Illinois | United States | about 16 hours ago
institutions depending on their expertise and active research projects. They also spend time at the SkAI Hub, which is located in the historic John Hancock Center in downtown Chicago, interacting with
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research and scientific excellence. Ready to be part of our team? Welcome aboard! Your future position: You will be working at the Department of English and American Studies as a member of the Fun*Cog
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Contract: Fixed term until March 2028 (possible continuation to an additional few years) Expected start date: 01 Feb 2026 or anytime soon Closed date: 23 Nov. 2025 (however, note that this will be
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(e.g. Google Earth Engine) for time series or large-scale analysis. Familiarity with ecological disturbance, vegetation stress, or fire science concepts. Experience working with large datasets and