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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 14 days ago
will work Christopher Knittel (Sloan School of Management and CEEPR). The project will develop empirical models of supply, demand, and international trade conditions for minerals required for the energy
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of the interactions between glaciers and oceans, with a focus on the European Arctic. Please read this interview for more details on Pedro’s work: https://ic3.uit.no/news/msca-physical-biochemical-modeling Within
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well as two insectaria (BSL-2/BSL-3). Eight research groups are currently based at the Institute of Immunology (IfI). Further information on the IfI can be found at https://www.fli.de/de/institute/institut-fuer
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running automated models to support analysis and decision-making. Working within an established technical environment, the Data Scientist helps improve existing models and supports the development and
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Associate 1 MS (22) Salary Range $61,500.00 - $91,875.00 Time Type Full time Duration Type Staff Fixed Duration (Fixed Term) Work Model On-site Background Check Requirements All candidates for employment will
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» Modelling tools Engineering » Control engineering Engineering » Simulation engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 23 Mar 2026 - 23:59 (Europe
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-85,800/year Type of Position Staff Position Time Status Full-Time Required Education PhD Click here for more information about equivalencies: https://hr.uky.edu/employment/working-uk/equivalencies Required
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as a role model, demonstrating characteristics and strategies crucial to college success. Effort is made to fill these positions with current or former SSS participants. Qualifications: Qualifications
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modeling. Positions may begin as soon as September 2025 . A Ph.D. in Mechanical and Aerospace Engineering, Civil and Environmental Engineering, Atmospheric and Oceanic Sciences, Geosciences, Computational
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. Conduct spatial distribution analysis of tribal and private lands and validate grid compatibility with existing data products. 30% Fit and evaluate statistical models to assess cannabis density patterns