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institutions; (ii) interact in a professional manner with industrial partners in a highly multidisciplinary environment; (iii) work to very tight deadlines and manage multiple projects; (iv) Report regularly
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Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest and thus requires
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information about AbbVie, please visit us atwww.abbvie.com . Follow @abbvie onX ,Facebook ,Instagram ,YouTube ,LinkedIn andTik Tok . Job Description Program Overview AbbVie needs outstanding individuals willing
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data knowledge and gain valuable clinical trials development experience. Our program is designed to expose you to multiple departments and cross functional teams. In your first year, you will work as a
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out from our world class teaching hospital at the Flinders Medical Centre in South Australia to multiple rural clinical locations all the way to Darwin in the Northern Territory. Our research and
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research university with a physical setting The New York Times calls “the most ambitious campus design program in the country.” With the launch of Next Lives Here, the Cincinnati Innovation District, a $100
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availability, project assignment, program rules, and availability of the participant. What are the appointment provisions? You will receive a stipend to be determined by ERDC-CHL. Stipends are typically based
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recently funded 5-year programme of antifungal drug discovery at the MRC Centre for Medical Mycology. This exciting new programme connects MRC CMM researchers with drug discovery experts and scientists from
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Australian National University | Canberra, Australian Capital Territory | Australia | about 13 hours ago
conduct research and deliver a research-led education program that encompasses the breadth of the sciences, supported by extensive international networks and by world-class facilities. To find out more
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for Multi-Agent Decision-Making, https://oceanerc.com ). This timely project will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making