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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
: 238003 Vacancy ID: PDS003562 Position Summary/Description: T32 Postdoctoral Fellow position. This is a one-year, time limited position with the possibility to extend to 2 years. This is a computational
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. Responsibilities* Design, implement, and evaluate LiDAR-based experiments in lab and real-world settings. Develop and test algorithms for object detection, tracking, and classification using LiDAR sensors. Help
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing
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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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the laboratories of Dr. Jason Rock in the Department of Ophthalmology and Immunology and Dr. Sören Müller in the Computational Sciences Center of Excellence at Genentech. The Rock lab combines in vitro and in vivo
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that directly relate to this position. The Draelos lab, in the departments of Biomedical Engineering and Computational Medicine & Bioinformatics, is seeking candidates for a postdoctoral research fellow position
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internationally known, unique facilities. This team has developed state-of-the-art experimental and computational models for solving water resource problems worldwide. CHL research and development addresses water
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. Interest in clinical algorithm development and dexterity with biostatistical coding in R or Python is a plus. The primary goal of this aspect of the CH CARE Study is to combine serially obtained somatic and