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collaborate closely with CCMA faculty on cutting-edge research projects involving design, analysis and implementation of advanced numerical methods and algorithms in interdisciplinary applications. The position
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, and uncertainty-aware algorithms for autonomous driving systems. The position focuses on developing robust decision-making, planning, and control methods that address uncertainty arising from perception
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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biomedical data science, developing new methodology and scalable algorithms, and collaborating with interdisciplinary teams at Duke. Duke is an Equal Opportunity Employer committed to providing employment
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a diverse team of biological and chemical scientists Experience with high performance computing environment (HPC) / cluster job submission Knowledge of statistical methods, data science algorithms
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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data
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Description Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only