[ES_JOBS_NET] ORISE USDA-ARS Postdoctoral Fellowship in Multidisciplinary Food Agency Modeling

[Friedrichsen, Claire - REE-ARS]

Good morning,

Dr. Katie Pisarello and I (along with Dr. David Archer) will be co-advising an ORISE SCINet postdoctoral fellow, and we were wondering if you wouldn't mind distributing this announcement to your Department?

The successful candidate will investigate and model food agency (related to food security) in Alaska at multiple spatial scales. This systems modeling work will be highly multi-disciplinary and will rely on big data integration and synthesis from fields such as plant physiology/agronomy, economics, and hydrology.

The ideal candidate would be "scientifically agile," able to work with different types of data from multiple sources spanning a variety of contexts, and would have experience with geospatial (GIS/remote sensing) datasets. The candidate should be quantitatively proficient and have some ability to work with mechanistic models and/or build statistical models.

If you know of anyone who would be interested and would fit this description, please direct them to the link below. Unlike other federal appointments, this one does not require US citizenship. Location is flexible (either Mandan, ND or Tifton, GA), including telework options.

We will begin reviewing applications on June 30th
Start Date is Flexible


Postdoctoral Fellowship Posting-USDA-Agricultural Research Service ORISE SCInet Postdoctoral Fellowship in Transdisciplinary Food Security Agency Modeling in Alaska

Research Project: 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 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 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 computing (HPC) clusters. The objective of this fellowship is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable to or requiring the HPC environment. Training will be provided in data science, scientific computing, AI/machine learning, and related topics as needed for the fellow to complete their research.

Throughout the course of this research project, the Fellow will have the opportunity to gain experience in two efforts where we will develop (1) a methodology for a baseline measurement of food agency in Alaska through the lens of biophysical and socioeconomic drivers, (2) a berry forecasting tool to predict berry growth with community-driven research with the Alaskan Quinhagak community. Food agency focuses on understanding the drivers of food security and insecurity and is the ability of an individual, community, or nation to meet their food acquisition and preparation goals. The Fellow will identify existing data (including geospatial/remote sensing data) and will conduct research to create a baseline framework for describing food agency in Alaska. The Fellow will have the opportunity to gain experience in 1) Data collection and integration from multiple sources, 2) Multi-disciplinary data harmonization for modeling purposes, and 3) Creative, empirically-based modeling approaches to address questions that are not yet integrated into existing mechanistic models. Specifically, the Quinhagak community relies on berry foraging as a large component of their food agency. Therefore, a berry forecasting tool will be developed as a specific application integrating biophysical and socioeconomic drivers to understand the impacts of climate change on traditional foraging locations as permafrost thaws and soil hydrology changes impacting Rubus chamaemorus / populations. The Fellow will collaborate with community members, tribal governments, and university partners to use geospatial data provided by partners to develop this community tool to predict Salmonberry harvests in the face of environmental changes.

Learning Objectives: The Fellow will have the opportunity to learn social science theory and methodology, transdisciplinary modeling, soil health, statistical and economic modeling. The Fellow will have the opportunity to be mentored by three supervisors spanning environmental modeling, big data management, statistical modeling, conservation psychology, anthropology, soil and water science, and agricultural economics. The main learning objective of the Fellow will be in the application of a social theoretical framework to analyze biophysical data and develop a baseline of food agency in Alaska. The Fellow will be encouraged to spend immersive time in Quinhagak, Alaska to meet with community members and learn firsthand about the importance of berry picking for Yuuyaragq. Additionally, the Fellow will have the opportunity to be an active scientist within the USDA-ARS NGPRL, SEWR and the University of Alaska, Fairbanks. The Fellow will also be able to take online courses in applied scientific tools, such as R, Python and statistics and to learn collaboration and leadership skills through workshop and working group experiences.


Monthly stipend: $7,911.97


This posting is now live and accepting applications at https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0228.







Sincerely,

Claire Friedrichsen, PhD
Research Social Scientist
Northern Great Plains Research Laboratory
USDA-ARS
Mandan, North Dakota
Cell: 701-321-3357




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