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kidney disease in adults: assessment and management. Clinical guideline [CG182]. Tangri N, Stephens LA, Griffith J et al A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure. JAMA
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quantitative live-cell imaging to probe and model these processes. By combining stem cell biology with cutting-edge microscopy and physical concepts, we aim to establish a predictive framework for tissue self
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biotechnology tools • Cognitive neuroscience • Psychological and brain sciences • Medical • Biomedical • Nanofluidics and microfluidics • Biomimetics and biofilms • Social media analysis and predictive modeling
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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, yield, and conversion strategies. Oversee predictive modeling, enrollment forecasting, and data analytics to support institutional decision-making. Strengthen partnerships with K-12 systems, community
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
neurological disorders with adult and/or pediatric patients, and with predictive testing for variable onset disorders. They should be comfortable seeing patients independently and using a variety of practice
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(earthquakes, landslides, and other gravitational instabilities). Develop advanced numerical models for improving the simulation, the analysis, and the prediction of geohazards, as well as the application
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in
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, with a primary goal of developing prediction models and identifying molecular endotypes for pulmonary diseases. These respiratory diseases include but are not limit to acute respiratory distress syndrome
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory