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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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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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learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
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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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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
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research using complex observational healthcare data, with a focus on cancer studies. The successful candidate will be expected to: Modeling multilevel survival data while addressing confounding and missing
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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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their communications Data Collection and Analysis: Collect data on the implementation process, including facilitator performance and participant feedback Analyze fidelity data to identify areas where
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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