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CORE A*/A conference paper. We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s
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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal
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workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
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The project: Non-destructive testing (NDT) is vital to ensure structures are safe and to reduce operating costs by facilitating predictive maintenance. The focus of this project is on improving
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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population changes, and other demographic parameters (survival, fecundity, reproductive success, etc.). These integrated population models (IPMs) are increasingly used in ecology. They offer clear advantages
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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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diversity expected under different conditions of resource competition. The post-doctoral fellow will develop new modeling frameworks, using R or a related language. Where to apply E-mail positions@gimm.pt
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, Python, and SAS or Stata) • Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling, experience with Medicare claims data • Clear scientific writing and
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clinical study to develop prediction models for periodontal disease progression (funded by the National Institute of Dental and Craniofacial Research). Primary responsibilities of the Study Coordinator will