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complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Computing Science at Umeå University is looking for a doctoral student in machine learning
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provides an accessible, quality education through flexible learning and unparalleled internship opportunities. At UA Little Rock, we prepare our more than 8,900 students to be innovators and responsible
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foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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and in vivo models. The prospective candidate will have opportunities to work within a highly interactive, multidisciplinary team. Preferences will be given to candidates who Have some relevant research
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, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
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Professor / Associate Professor / Assistant Professor / Research Assistant in Quantitative Marketing
inference, or machine learning techniques; and (ii) are open-minded and committed to teaching excellence at both undergraduate and graduate levels. Applicants for the Research Assistant Professor position
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the Manchester BHF CRE: Geometric Deep Learning for Complex Manifolds: Novel deep learning theories, models and architectures to simulate interactions within non-Euclidean, patient-specific cardiovascular
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health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 18-Mar-26 Location: Boston, Massachusetts Type: Full-time Categories: Academic/Faculty Computer/Information Sciences