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: The SCINet/Big Data Research Participation Program of the USDA ARS offers research opportunities to motivated postdoctoral fellows interested in solving agriculture-related problems at a range of spatial and
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, Python, etc.). Demonstrated skills in developing statistical approaches for analyzing spatial and temporal data. Proficiency in processing and analyzing meteorological data. Knowledge of code and software
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-clinical models and multi-omic approaches. The proposed research involves the application of state of the art methods such as single cell RNA-Seq/ATAC seq/spatial transcriptomics on preclinical animal models
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specific focus on seasonal succession of host plants. Additionally, knowledge of agronomy, agricultural practices, and spatial technologies (e.g., global positioning systems – GPS) would be advantageous
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the one of the relevant fields. Preferred skills: Experience in forest operations analysis, forest resource management, spatial data science, supply chain/logistics analytics, machine learning Analytical
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Project: The Postdoctoral Program of the USDA ARS offers research opportunities to motivated postdoctoral fellows interested in solving agriculture-related problems at a range of spatial and temporal scales
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use a range of spatial and analytical software for data analysis to include ArcGIS, Kaleidoscope Pro, and R. Other activities will include entering data, transferring acoustic files, data management
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the participant’s educational and professional background and help the participant gain knowledge in processing and analyzing large volumes of bioacoustics data. Participants will use a range of spatial and
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techniques for spatial prediction and mapping of forest species composition at the watershed and larger scales. Develop novel analyses to assess the effects of prescribed fire and understory removal on forest