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: Your role at the lab is to investigate the behavior of individual molecules and bio-molecular condensates in living cells. You will use and develop the imaging and computational methods that to visualize
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laboratories of Dr. Juliana Vasco-Correa and Dr. Christine Costello on multiple funded research projects, performing computational modeling of crop and livestock production and forestry systems, and
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information science, and applied social science research. This position is well-suited for someone interested in research design and the application of geographic principles and methods to policy analysis and
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detailed and accurate records. Ensure compliance with environmental, safety, health and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
or high-performance computing environments. - Strong background in statistical modeling and machine learning, especially applied to spatiotemporal data. - Familiarity with urban systems, urban emissions
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Driver's License: A driver's license is not required for this position. More About This Job Required Qualifications: A PhD in Genetics, Bioinformatics, Computer Science, Data Science, Statistical Genomics
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research objectives, under the direction of a supervisor. Demonstrated expertise in hydrologic, water quality, crop, and ecosystem modeling. Strong computer and statistical programming skills, with
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degree (PhD or equivalent) in computer science, data science, statistics, bioinformatics, or a related discipline A strong publication record in machine learning, computer science, bioinformatics
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· An environment encouraging curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills · Multicultural and international
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying