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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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) for science with Dr. Aleksandra Ciprijanovic (alexciprijanovic.com) and her research group! The successful candidate will join a multidisciplinary team working at the intersection of deep learning, cosmology
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.. Normandale is the largest community college in the Minnesota State Colleges and Universities system. To learn more about our strategic vision, visit http://www.normandale.edu/Normandale2020. Connections
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biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical, recurrent, or developmentally inspired models
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Is the Job related to staff position within a Research Infrastructure? No Offer Description You are keen on contributing to new advances in deep learning methodology for cardiac ultrasound imaging
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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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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 18 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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scoping to deployment and monitoring of production-grade models—with a focus on both Generative AI and Deep Learning. The ideal candidate holds a Ph.D. in Deep Learning or Generative AI and brings a strong
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to recruit a research assistant to work on the intersection of compilers and deep learning. Many companies, such as Google, Facebook, and Amazon are building new specialized programming frameworks. This is
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across the university. The RAD Collaboratory will be comprised of different research areas, each led by a faculty area lead. The vision of the RAD Collaboratory is “Deep Learning, Deep Connections