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Field
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in radiotherapy with the goal of enabling fully adaptive radiotherapy. The work is based on deep learning, where models are trained on generated or clinical data. The project is carried out in
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integrates neuroimaging, sleep measurement, digital phenotyping, electronic health record (EHR) data, and deep clinical phenotyping to identify predictors of symptom trajectories and functional outcomes in
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
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, multimodal foundation models, continuous learning systems, or agentic AI models. Experience with state-of-the-art multimodal foundation models and agentic AI frameworks Experience in large-scale deep learning
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
Deep Learning), successful experimental collaboration experiences and excellent communication skills are preferred. Create a Job Match for Similar Jobs About The University of North Carolina at Chapel
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transportation systems and autonomous driving. • Strong understanding of generative AI, deep learning, and multimodal machine learning, with hands-on experience. • Excellent programming skills and proficiency with
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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
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch
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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal