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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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A Postdoctoral Research Associate position in advanced MRI acquisition, analysis, and modeling is available in the Department of Radiology at the University of Virginia School of Medicine, under
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cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery
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disparities for communities and populations. Candidates will receive mentorship and training in precision health, including advanced statistical methods focused on predictive modeling in relation to response
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techniques for pulmonary diseases such as COPD, interstitial lung disease, asthma, pulmonary hypertension, and cystic fibrosis in human and preclinical modeling, including murine models. Postdoctoral
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of national research infrastructures Evaluating the evolution of Generative AI performance over time and across tasks Analyzing international AI models and their representations of the U.S. in global discourse
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science of science, network science, and natural language processing. As part of a small research team, the postdoc will help lead efforts to provide a quantitative model of global competitiveness
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ability to learn new software quickly and introduce new useful applications and platforms to the team. Proficient in advanced statistical modeling software (e.g., R, Stata, Matlab) Physical Demands This is
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normal and cancer cells, and mouse models. Research in the Trinh lab centers on protein- and RNA- mediated gene regulation via modulating chromatin architecture in development of immune cells, particularly
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molecular mechanisms of carcinogenesis from a developmental perspective using Neurofibromatosis Type 1 (NF1), a common tumor predisposition human genetic disorder, as a model. While working on