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Observatory (Part Time) Posting Number req25571 Department Steward Observatory Department Website Link https://astro.arizona.edu/ Location Steward Observatory Address 9040 S. Rita Rd, Suite 1500, Tucson, AZ
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ability to set priorities, meet deadlines, and complete tasks and projects on time and within budget and in accordance with task/project parameters. Technical Skills: Demonstrated proficiency and
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illumination variations, which introduce non-stationary shifts and degrade the performance of conventional models. The project proposes the use of hypernetworks to dynamically adapt the parameters of the gaze
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INESC COIMBRA - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES DE COIMBRA | Portugal | about 9 hours ago
1 Apr 2026 Job Information Organisation/Company INESC COIMBRA - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES DE COIMBRA Research Field Engineering » Computer engineering Researcher Profile
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Your Job: The CROP (Combining ROot contrasted Phenotypes for more resilient agro-ecosystem) project aims to estimate the beneficial impact of combining contrasting wheat root phenotypes in the same
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for equal periods or until the term of the project, with an estimated starting date in July 2026, according to article 13 of the Regulation and article 3 of the Statute, under an exclusivity regime, except
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and is the point of contact for actively managing external vendors and affinity partners to: develop and outline contract parameters, establish event timelines and deadlines, select appropriate media
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equivalencies: https://hr.uky.edu/employment/working-uk/equivalencies Required Related Experience No experience required. Required License/Registration/Certification None Physical Requirements This position
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parameters is a must. Candidate will also need strong organizational, prioritization, basic computer navigation skills, and a working knowledge of the Internet. Does this position have supervisory
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of Barcelona; Particle Physics Phenomenology group. Main responsibilities / tasks: 1. Develop anomaly detection methods using Machine Learning and Simulation-Based Inference for high-dimensional parameter spaces