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studies. Develop and apply advanced statistical methods and machine learning techniques using tools such as R and Python. Integrate and run process-based models (e.g., crop models, hydrologic models
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data analysis. Record of scholarly publications and scientific communication skills Proficiency in statistical and data analysis tools (e.g., MATLAB, Python, R). Preferred Qualifications: Experience with
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Required Qualifications: An earned doctorate (foreign equivalent acceptable) in horticulture, agronomy, soil science or a closely related discipline is required. Skills in statistical methods, turfgrass
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loads on these farms. The project includes advanced statistical tools and modelling to evaluate the influence of soil properties, historical and current land use, and environmental conditions on water
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Classification Title: Postdoctoral Associate Classification Minimum Requirements: A Ph.D. in biomedical informatics, computer science, information science, data science, (bio)-statistics, (applied
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science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM fields. Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep
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research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a variety of statistical and machine learning techniques – one
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. Experience leading investigations linking simulations to observational data. Experience with statistical characterization of data, preferably within a Bayesian framework. Job Description: A Post-doctoral
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historical CORONA satellite imagery Integrate multi-source datasets including GEDI LiDAR and GLOBE citizen science observations Apply cutting-edge geospatial and statistical modeling techniques to quantify
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experience, including proficiency in statistical applications, is a plus. Candidates with a strong interest in conducting research focused on a) decreasing or eliminating health disparities among medically