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integrating advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques with process-based crop models, this research will empower farmers to optimize conservation practices, increase
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avian influenza (HPAI) airborne transmission between U.S. poultry facilities. The primary focus of this opportunity will be learning to develop statistical and mathematical models to assess airborne
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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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, microbiology, infectious diseases, animal agriculture, or food safety. Experience in applied statistics, data science, machine learning, mathematical modeling, epidemiology, disease ecology, and PCR assay
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engineering, as well as computer vision, imaging, hyperspectral imaging, machine learning techniques in food and agricultural area, is desired. A understanding of modern AI model development Stipend $70,000.00
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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
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skills: Experience developing artificial intelligence (AI) or machine learning (ML) models, particularly for time series or spatiotemporal data. Experience with representation learning, anomaly detection
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) in Beltsville, MD conducts a combination of modeling and experimental research focusing on crop and soil response to abiotic factors and agroecosystems management. Research Project: During
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inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest
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. Research Project: A Postdoctoral Fellow will participate in a project focused on analyzing forest change and disturbances on U.S. forests. In this project, we will collaboratively develop one or more models