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efficiency. Experience working with R or Python data science tools to analyze and visualize health care data and to develop data pipelines to support machine learning model development. Experience working with
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-based ecosystem modeling, MRV/CDR, nature-based solutions, or food–energy–water systems. Experience with machine learning, remote sensing, or near–real-time environmental data systems. Experience
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predictive modelling; Bioinformatics and Knowledge Graphs (visualization and reporting); AI-based data integration across cohorts (with federated machine learning); Contribute to ongoing projects, such as: o
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new programming languages, libraries and technologies. Any prior experience working with frontend/backend web development, machine learning, or high performance computing would be desirable, as would
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to oversee research activities outlined in NSF Grant 2520154 “Understanding Expectation-Driven Learning in Early Childhood: An Experimental and Computational Investigation,” under the supervision of Dr. Kimele
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the research activities of Pratt Institute under one roof in the Brooklyn Navy Yard (BNY). Since our founding in 1887, Pratt has upheld the belief that education should be accessible to all who wish to learn. As
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) hydrological/hydraulic modelling of urban streams and adaptation measures for current conditions and future climate scenarios. The results from the project will contribute to better estimations of how urban
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–outflow measurements, providing limited insight into what happens inside the systems and leading to substantial uncertainty in design, modelling, and long-term performance predictions. This PhD project aims
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling