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for transportation prediction, system optimization, and environmental/health impact modeling Deployment of decision-support tools for public-sector clients (municipalities, MPOs, DOTs) Urban mobility, equity
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limited to, integrated cancer risk prediction model, cancer subtype discovery, genomic and epi-genomic data integration, single cell RNA-Seq data analysis, non-coding RNA data analysis, and other datasets
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 6 hours ago
lab results to predictive reservoir models. Through this project, the participant will learn about hands on lab experience, modeling, and presenting and publishing results. The participant will: (1
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initiatives include developing advanced aqueous emulsion and suspension systems for spray coating, predictive modeling of packaging performance, and optimizing packaging designs for high-value product
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team. The publication pipeline includes papers on main trial outcomes, secondary outcomes, moderators of treatment effects, and prediction based on baseline data. Research tasks The main tasks will
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. The individual is required to use tools in chemical ecology, genomics, molecular biology, and AI-driven predictive modeling in the research project. Specific duties will include: (1) gather and merge existing data
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mechanobiological models of bone healing will help create a robust model, able to predict long term surgical outcomes in patient specific cases. The results of the validated model will provide insight
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. Close collaborations are expected with researchers from across ORNL with expertise in materials and nano science as well as AI and computational science. The scientific effort will both predict materials
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under different climatic and social contexts to provide decision-making tools for sustainable urban planning. Main Tasks and Responsibilities: Develop a predictive model integrating the direct and
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models