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-based techniques (e.g., deep neural networks) will be used to automatically learn the system dynamics and the modelling errors, as well as to obtain an automatic tuning of the cost parameters/constraints
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(PyTorch, scientific Python) with solid experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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the dynamics of potentially illegal waste deposits. The research will apply deep learning and computer vision techniques to identify regions within an image where there is an increase or decrease in
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are the lead technicians for all events that occur at The O'Shaughnessy. While each employee may be assigned to a specific specialized area of the production department, general knowledge and deep skills in all
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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 2 days ago
, Approximation Theory, Machine Learning, Inverse Problems and Regularization Theory. Proficiency in programming with a strong preference for Python and deep learning frameworks such as PyTorch is highly desirable
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Macomb Community College teaching Faculty demonstrate deep subject matter knowledge and provide effective instruction to students using various modalities including, but not limited to, on campus, online
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: Required: • MSc (or equivalent) in: Computer Science, Cybersecurity, Machine Learning, or related field • Strong background in: machine learning / deep learning, mathematics (probability, linear algebra
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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learning and deep learning models for renewable energy forecasting, particularly wind power generation. Working with data-driven weather prediction models and high-resolution meteorological datasets