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gliomas. The project integrates generative AI, interpretable models, uncertainty estimation, and federated learning to improve clinical decision-making, data privacy, and personalized medicine
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
experience with a modern deep-learning framework such as PyTorch or TensorFlow. Experience developing, training, and evaluating machine-learning models using complex biomedical, imaging, or other high
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background in Control Systems Theory (e.g., MIMO control, state-space methods, optimal control, model predictive control, system identification) AND/OR Artificial Intelligence / Machine Learning / Data Science
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, ECG, GSR and other biosignals. Multimodal data analysis Machine learning and advanced statistical modeling Research experience in perception, consciousness, cognition, or emotions will also be valued
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learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing
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, and machine learning; and the development of ecological indicators for environmental monitoring in the Campos and Santos Basins, Brazil. Mandatory requirements Applications from people with training in
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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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and meteorological modeling. Experience running or maintaining operational/automated forecast systems. Experience applying machine learning methods to air quality or geophysical data (e.g., forecast
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in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing systems, data collection
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Chemistry to conduct fundamental and applied research at the intersection of machine learning and computational chemistry. In this role, you will develop physics-informed, symmetry-aware models