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National Center for Toxicological Research (NCTR) | Jefferson, Arkansas | United States | about 11 hours ago
will provide important, mechanism-based data for risk assessment. This supports FDA's mission to protect public health by advancing predictive, human-relevant models for chemical safety evaluation
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decision-support tool that quantifies the accuracy of mechanistic electrophysiology model predictions for drug-induced changes to electrophysiology observed in animal cardiovascular safety studies. We
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well as AI-driven approaches for treatment outcome prediction and clinical decision-making. Projects involve multimodality imaging data, including CT, MRI, PET, and Ultrasound, as well as clinical radiation
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acquired across multiple anatomical regions to form a patient-level assessment. This PhD project will investigate novel deep learning methodologies that jointly model anatomical structure and prediction
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climate change pest research / collaborate in open, inclusive biophysical ecology group / deep computational work with the Julia programming language Apply now to shape predictive models that guide pest
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and predictive modelling into food innovation, contributing to meaningful industry outcomes while helping create change together across agriculture and food systems. Key responsibilities will include
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, to becoming more involved in the bioinformatic and analytic aspects of predictive modeling. As such, the Postdoctoral Computational Biologist should have a willingness to learn new languages and tools as the
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 11 hours ago
data using statistical methods, contributing to the development of reference compound libraries for predictive model validation, and developing AI, statistical, or mechanistic models to translate
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focus on foundation models, medical image analysis, and multimodal healthcare data integration. In this role, you will: Develop novel federated AI methodologies, foundation model adaptation techniques
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association studies, and interpret their results in the context of economically important traits. Evaluate and improve genomic prediction models for use in cultivar development programs. Gain experience