USDA-ARS Postdoctoral Fellowship in Multi-Scale Image Analysis for Patterns in Phenology: New Mexico

Agency
U.S. Department of Agriculture (USDA)
Location
Las Cruces, New Mexico
Job Category
Post Doctoral Appointments
Salary
TBD
Last Date to Apply
06/30/2022
Website
https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0077
Description
*Applications will be reviewed on a rolling-basis and this posting will remain open until filled. ARS Office/Lab and Location: A postdoctoral fellowship is currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Jornada Range Management Research located in Las Cruces, New Mexico. For more information on the Jornada USDA ARS Range Management Research, please visit: https://jornada.nmsu.edu/ This research opportunity is part of the SCINet Fellowship program at ARS. All postdocs will spend time at headquarters for some of their training, but will be based at ARS regional laboratories for more specific training. One of the goals of this research opportunity is to encourage cross-disciplinary, cross-location research; this will be done by placing postdocs in different regional labs based on their skillset and interests in regional locations. The strength of this fellowship program is the collection of postdocs and ARS' collection of regional labs. Research Project: The SCINet/Big Data Program at ARS offers research opportunities to motivated postdoctoral participants interested in solving agricultural- and natural resource-related problems at a range of spatial and temporal scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help solve complex agricultural problems that also depend on collaboration across scientific disciplines and geographic locations. In addition, many of these technologies rely on the synthesis, integration, and analysis of large, diverse datasets that benefit from high performance computers (HPC). The objective of these opportunities is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each participant and amenable to or required by the HPC environment. Training will be provided in specific AI, machine learning, deep learning, and statistical software needed for the HPC. Under the guidance of a mentor, the selected participant will be involved in organizing and co-leading a multi-disciplinary effort to develop and refine models of ecosystem productivity, carbon dynamics and plant phenology to quantify effects of land management and climate on productivity in agro-ecosystems nationwide. This research opportunity will involve integration of multiple disparate data streams including eddy-covariance, remotely sensed imagery from satellite and near-surface cameras, and land management records from multiple research networks including LTAR, NEON, Ameriflux, and LTER. Learning Objectives: The participant will gain experience with analysis of multi-scale imagery and data assimilation methods to refine models with field observations of phenology and productivity. The participant will also receive training in methods associated with high performance computing. The participant will learn how to contribute to the development of and co-lead ARS-wide workshops to synthesize and integrate multi-scale ground- and satellite-based imagery for phenology-related data, and will help organize a community of scientific practice on this topic. The participant will also have the opportunity to collaborate with multiple USDA ARS scientists on data analysis projects, and to write collaborative scientific papers dealing with phenology, broad-scale climate change models, and forecasting tools. Mentor(s): The mentor for this opportunity is Dr. Dawn Browning (dawn.browning@usda.gov). If you have questions about the nature of the research please contact the mentor(s). Anticipated Appointment Start Date: As soon as a qualified candidate is identified. Start date is flexible and will depend on a variety of factors. Appointment Length: The appointment will initially be for one year, but may be renewed upon recommendation of ARS and is contingent on the availability of funds. Level of Participation: The appointment is full-time. Participant Stipend: The participant(s) will receive a monthly stipend commensurate with educational level and experience. Citizenship Requirements: This opportunity is available to U.S. citizens only. ORISE Information: This program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and ARS. Participants do not become employees of USDA, ARS, DOE or the program administrator, and there are no employment-related benefits. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE. Questions: Please visit our Program Website. After reading, if you have additional questions about the application process please email USDA-ARS@orau.org and include the reference code for this opportunity.
Qualifications
The qualified candidate should have received a doctoral degree in one of the relevant fields. Preferred skills: - Experience with remotely-sensed data, modeling of spatial and/or time series data, and land surface modelling - Experience working with large datasets and data mining approaches - Experience with database and application development - Understanding of plant ecology and phenology - Proficiency in R and python - Strong oral and written communication skills
Contact Person
USDA-ARS@orau.org
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