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data visualizations using common libraries like D3.js, ggplot, or vega, as well as expertise in languages and libraries for data analysis such as R or Python. Lastly, candidates should have experience
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languages and data analysis tools such as Python, Matlab, and R. Proficiency in GIS software (e.g., ESRI ArcPro). Strong written, oral, and visual communication skills. Responsibilities Research- Under
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
, designing tools for data interpretation and visualization, and/or training foundation models and large language models (LLMs) for diverse biomedical applications. Dr. Li has published over 70 peer-reviewed
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Ph.D. (or equivalent) in Astronomy, Astrophysics, Physics, Planetary Science, or a closely related field by the start date. Demonstrated experience in one or more of: large-scale data analysis, time
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, and application of advanced data analysis tools to chromatographic data. Demonstrated proficiency in pyrolysis-gas chromatography/mass spectroscopy, size exclusion chromatography, and ion chromatography
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of molecular sensing circuits Designs and carries out experiments in BSL2 lab space Performs quantitative analyses and generates data-rich visualizations from data Demonstrates ability to perform
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of Byzantine Art History (Faculty of Historical and Cultural Studies). The research focus is on the analysis of Byzantine visual art, taking into account iconographic, stylistic, and cultural-historical
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in the visualisation of analysis results of the acquired data; preparing publications and presenting results; collaborating with other team members. This is a half-time position for a post-doctoral
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writing) is mandatory. 4. Strong ability in data analysis and visualization is required. 5. Knowledge of French or Chinese is an asset. • International Experience: Prior research experience abroad is highly
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automating geospatial workflows. Proficiency with GIS tools (e.g., QGIS, ArcGIS) for spatial analysis and visualization. Experience developing statistical or machine learning models to estimate fuel moisture