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://www.hercjobs.org/regions/higher-ed-careers-upstate-new-york/ , as well as additional information at https://www.hamilton.edu/dof/faculty-development/resources-for-prospective-or-new-faculty/opportunities-for-spouses
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State Department of Human Development and Family Studies, located in the College of Health and Human Development, is actively seeking applications for an open-rank, tenure-track or tenured faculty
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://www.hercjobs.org/regions/higher-ed-careers-upstate-new-york/ , as well as additional information at https://www.hamilton.edu/dof/faculty-development/resources-for-prospective-or-new-faculty/opportunities-for-spouses
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of artificial intelligence and continuous monitoring in bridge engineering" aims to develop an integrated methodology for proactive bridge management, moving from periodic inspections to data-driven maintenance
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on Youth, Fam & School-4425 Description of Work: The Proposal Development Coordinator joins an interdisciplinary team of research development professionals to provide comprehensive proposal development
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Technologies INspiring Young scientists (DESTINY2, https://www.destiny-phd.eu/ ) is opening 24 doctoral positions hosted by universities, research centres, and laboratories in France (17), Spain (3), Germany (1
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development — core technologies for the next decade of AI-powered engineering. This is a collaborative research project with Atlassian. Check out some recent work: - https://www.atlassian.com/software/rovo-dev
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Modeling, Analysis & Prediction of Particle-laden Real-Gas Supersonic Turbulence. (Ref. 10267290001)
Description Mission: Carry out the modeling, analysis and prediction of real gas supersonic turbulence. Fuctions to be developed: Develop tools to aid analysis. Perform experimental and computational analyses
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stipend (2025/26 rate) per annum (plus additional financial support to cover training and development opportunities). Location: Open University Campus, Milton Keynes This full-time PhD project is available
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics