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, computational pathology, and spatially resolved multi-omics data. The system will leverage generative models like diffusion models and Variational Autoencoders (VAEs) to simulate phenomena and predict outcomes. A
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-the-loop control for extreme robotics applications, including high performance algorithms for 3D perception, model predictive control, reinforcement learning, generative AI, and simulation and virtual
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deep learning models to predict and analyze large-scale orbital capability. - Evaluate and optimize the performance of the models, comparing them with traditional orbital analysis methods. Where to apply
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member of the UIS Campus Senate Committee on Recruitment and Retention (CARR). Serves on the Illinois Council of Enrollment Managers. Utilizes state, regional, and national data to create predictive models
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environment. Development of models to diagnose and predict battery performance and ageing. Participation in national and international research projects related with energy storage and its integration in
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recover quickly from disruptions. The research will involve reinforcement learning, predictive modeling, and real-time adaptive control to dynamically optimize production sequencing, resource allocation
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pain, a critical and currently missing component in translational research. These new models are intended to enable accurate prediction of analgesic efficacy and disease-modifying effects of novel
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development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for identifying high risk individuals and groups. The PDRA will translate conceptual
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understood. Most current assessments are based on inflow–outflow measurements, providing limited insight into what happens inside the systems and leading to substantial uncertainty in design, modelling, and
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, we aim to create autonomous “self-driving” microscopes that: build statistical models of biological dynamics in real time predict the most informative next experiment execute it automatically on living