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environment. 4) Development of machine learning and deep learning models for forecasting reduced visibility, low cloud ceilings, and adverse weather conditions impacting air operations. 5) Implementation
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mathematical foundations needed to make deep operators reliable, robust, and applicable for control of complex engineering systems. In this PhD project, you will investigate how operator-learning models can
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 24 hours ago
of proficiency in either R or Python in the areas of machine learning, deep learning, statistical analysis, computer vision, and/or graph analysis Experience with data engineering to create data
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patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference to identify strategies for restoring immunological homeostasis. Together
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science, artificial intelligence, or a closely related field. A strong background in machine learning and deep learning. A good understanding of Transformer architectures and modern AI models. Good programming skills
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to the group’s open-source software, and participation in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, computer science
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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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function based on a coupled NEMS network, consisting of 2 or more double-drum resonators. This is beyond current state of art and relies on deep understand of more degrees of nonlinear complexity
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at least one deep-learning framework (PyTorch preferred).•A solid grounding in machine learning. Experience with representation learning, generative models, foundation models or multimodal integration is a