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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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Electrical Engineering, Computer Science, or a related field Strong background in speech processing, signal processing or machine learning Proficiency in Python and deep learning frameworks Experience with far
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IUT, in Diophantine geometry, in applications of modern mathematics to deep neural networks, and related areas. The previous research work has been partially supported by research grants from several
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, e.g. experience fitting Reinforcement Learning models or applying Agent Based Modelling to human behavioural data. You should have a deep understanding of the strengths and limitations
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Electrical Engineering, Computer Science, or a related field Strong background in speech processing, signal processing or machine learning Proficiency in Python and deep learning frameworks Experience with far
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supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. Responsibilities: -Develop and implement advanced numerical algorithms and deep learning
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how