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climate. Observational work would include data from GRACE, SMAP, GPM or in-situ stations. Model diagnosis and analysis can include CLM, VIC, CLSM, and LIS frameworks including GLDAS and NLDAS. Model
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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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or Bachelor's Degree received within the last 60 months or currently pursuing. Discipline(s): Computer, Information, and Data Sciences (16 ) Life Health and Medical Sciences (45 ) Mathematics and Statistics
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Citizenship: U.S. Citizen Only Degree: Master's Degree or Doctoral Degree received within the last 60 months or anticipated to be received by 12/31/2026 11:59:00 PM. Discipline(s): Computer, Information, and
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analysis of large, diverse datasets including field experimental data, geospatial data, and time series data. Experience with machine learning and statistical learning. Familiarity with various management
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. Develop skills in coupling crop and hydrology models at watershed scales. Gain experience validating models using large, multi-source datasets. Learn to apply high-performance computing and machine learning
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, visit the ORISE Research Participation Program at the U.S. Department of Defense . Qualifications The qualified candidate will hold a PhD degree in Biomedical Science, Bioinformatics, Data Science
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research applying artificial intelligence (AI) and machine learning (ML) techniques to analyze cervid movement patterns. GPS telemetry data obtained from free ranging cervids will be used by the participant
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. Data management skills and demonstrated experience managing, standardizing, synthesizing, and visualizing large, disparate natural resource (e.g., forest inventory, climate, land use, soils, elevation
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generated quickly and regularly. Help develop machine learning techniques for feral swine abundance in data sparse environments. Collaborate with APHIS Wildlife Services (WS) to integrate data and model