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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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preparation. Familiarity with initiating and maintaining primary cell cultures. Knowledge about statistical methods. Ability to follow oral instructions. .About The University of Texas at Tyler and UT Tyler
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experience with remote sensing data and its analysis, and geospatial skills Strong statistical background Excellent English writing and verbal communication skills Ability to travel periodically for reports
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The project will require coding for data analysis and statistics, as well as NGS data analysis. Experience with either of these would be advantageous but an eagerness and commitment to learn is more important
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request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce. This information will be
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there. There are also some postdoc stipends available which are not attached to any of the above mentioned centers/grants. Note that we also have a different call in Statistics, Insurance and Economics, including
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cognitive neuroscience. You have documented experience in experimental research on learning or decision-making with knowledge of computational models (e.g., reinforcement learning). Good statistical skills
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design, statistical sampling and analysis of large, multi-taxa biodiversity datasets. Expertise in landscape-level biodiversity and production analyses using R, QGIS, Google Earth Engine. Extensive
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information, contact Dr. Jun Wang at jun-wang-1@uiowa.edu . Education Requirement: Ph.D. (or equivalent) in Atmospheric Sciences, Chemical Engineering, Environmental Engineering, Applied Mathematics/Statistics
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they would design, organize, and conduct advanced quantitative (statistical, spatial, etc.) analyses relevant to climate equity, environmental justice, and/or environmental health; translate complex data and