525 postdoc-in-thermal-network-of-the-physical-building positions at Princeton University
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well as an integral member of the AI Lab and the research community it supports. You will work with a diverse group of faculty, postdocs, and students from multiple disciplines. If you are passionate about advancing AI
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at the intersection of Engineering and the Life Sciences. ODBI is evolving rapidly, including launching various programs and building physical and programmatic infrastructure. ODBI is seeking a scientific staff member
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Seeking a temporary Research Associate in machine learning. Pursue research in machine learning. Work on new forms of diffusion-based generative models. Meet with postdocs Participate in group
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-on management responsibilities. This technical leadership role requires close collaboration with cross-functional teams, including OIT, research staff, facilities, building management teams, and external vendors
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will address the emerging need for AI/ML in fusion, other areas of plasma physics, and computational sciences. In addition, the incumbent will work with CSD Leadership to make key hires, build core
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, analytical, and problem-solving skills. Ability to work effectively and collaboratively as part of a team. Excellent organizational skills. Detail-oriented and able to make connections between similar problems
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the architect selection process in concert with HRES and the Facilities Procurement Office for projects. The Project Manager will manage residential renovation and selected maintenance projects up to $2M from
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custodial work; set-up for special events; ordering and picking up supplies; painting building spaces; taking and maintaining inventory of non-art items and publications at the Museum and offsite storage
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individual and make an impact in a place where people, quality and value mean everything. In our campus dining operations, the selected candidate will provide customer service within the parameters
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
sea ice model; 3) evaluation of the impact of the conservative network in fully coupled historical and scenario climate simulations; and 4) the development of an emulator for sea ice model physics