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Field
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that amplify human potential. The successful candidate will engage in innovative research projects in ML, focusing on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems
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intended support design decisions related to manufacturing and process parameter selections; known to influence the material microstructure. Duties: The post doc will develop and apply computational methods
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
of models and observational data using Python, Matlab, R, etc. Expertise required in developing new models, algorithms for dispersion, gas-phase chemistry and/or aerosol microphysics model Preferred
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
) strong communication skills – written and oral, c) ability to develop/translate model algorithms and develop new model code in Fortran, d) software skills needed to work with multiple observed and model
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external) provides algorithmic and data-driven solutions providing fairer access to public educational resources. The lab enables school districts to optimize large-scale equitable resource allocation
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unravel the complex relationships between land use changes and fire regimes over the past 60 years. The successful candidate will lead efforts to: Develop advanced deep learning algorithms for classifying
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research studies for automated image analysis. In particular, you will: Plan, develop, and implement AI/ML algorithms for pathology image analysis. Integrate multi-modal data (e.g., genomics, clinical data
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molecular biology. This research opportunity will be focused primarily on developing tightly integrated sequencing experiments and computational algorithms to characterize biomolecular interactions with high
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omics to advance biological and clinical discoveries and develop next-generation theragnostics. The postdoctoral fellows will mainly focus on (1) creating novel computational algorithms to analyze and
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across the value chain. Using Bayesian Optimization / Modern Design of Experiment, we build the data-foundation to enable true hybrid development between humans and advanced learning algorithms such as