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Field
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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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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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Artificial Intelligence (applied mathematics, computer science, etc.), or a thesis defense scheduled for 2025. • Research contributions in deep learning, statistical learning, natural language processing (NLP
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located in the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Our deep-rooted commitment to excellence, collaboration, integrity, diversity and
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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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should be highly proficient with a range of machine learning approaches, including unsupervised, semi-supervised, supervised, and various deep learning techniques (e.g., convolutional neural networks
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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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foundational and applied topics in computer vision and machine learning, with particular strengths in inverse problems, generative models, and geometric deep learning. We work across diverse application areas
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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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