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Scotland Innovation Hub to provide a secure cloud computing platform for Federated Learning and Machine Learning model development, and clinical researchers from NHS Greater Glasgow and Clyde. The successful
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in a university or college setting. Working knowledge of the content areas of probability, statistical methods, generalized linear models, statistical computing, and machine learning. Preferred
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated communities in
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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work on adapting or developing marine foundation models. Self-supervised learning and active learning are also possible research topics. You can also focus on challenges related to modelling physics
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Director, Community Engagement, Constituent Engagement, Duke Alumni Engagement and Development (AED)
retirement benefits, and a wide array of family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity
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Python. Other requirements to consider: Machine Learning knowledge. Specific Requirements Knowledge: Fluid Mechanics. Computational Fluid Mechanics (CFD). Aerodynamics. Wind turbines. It will be assessed
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) are required - Experience in working with Earth system model simulations is required - Experience in machine learning is required - Willingness to travel for work (project meetings, workshops, and research
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of Artificial intelligence for De-Novo molecular design Machine learning/Neuronal networks to develop novel drug discovery tools Molecular modeling and simulation Theoretical biophysical medicinal chemistry Deep