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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
Filled No Position Type Permanent Staff (EHRA NF) Working Title Research Software and AI Engineer Appointment Type EHRA Non-Faculty Position Number 20075049 Vacancy ID NF0009612 Full Time/Part Time Full
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. Experience in, or willingness to learn data-driven approaches, including artificial intelligence (AI) and machine learning (ML) models, to solve problems. The Successful Candidate Will A curious scholar with a
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 2 months ago
demonstrated the efficacy of employing machine learning approaches to furnish engineers with a rapid computational asset for structural design and monitoring. Given that numerical models incorporating multi
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building technology, HVAC system study and optimization, sustainable building design, and building energy modelling, for built environment applications is preferred. Knowledge in test instrumentation, PLC
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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the EU’s ambitious AI Factories initiative. Learn more: https://mimer-ai.eu/about-mimer/ , https://www.naiss.se , https://eurohpc-ju.europa.eu/ai-factories_en The position As AI Training Program Officer, you
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professional programs, including P&L management, enrollment-driven revenue forecasting, and expense modeling. Lead monthly and annual financial reviews, translating complex financial data into actionable
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flexible role not tied to any single predefined project. The successful candidate will contribute to a range of modelling-driven projects that tackle key questions in cancer biology, such as understanding
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already been awarded a PhD degree. Selection process You should submit your CV through a dedicated site: https://cv.newton-6g.eu/ Additional comments Position: Data-driven models for CF networks
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experimentally driven (approximately 70/30 wet lab to modeling) and will include: Design and fabrication of 3D-printed brain tissue models with tunable transport properties Development of experimental methods