USDA-ARS SCINet Postdoctoral Fellowship in Modeling the Spread and Adaptation of Stored-Product Insect Pests: Kansas

U.S. Department of Agriculture (USDA)
Manhattan, Kansas
Job Category
Post Doctoral Appointments
Last Date to Apply
*Applications will be reviewed on a rolling-basis and this posting could close before the deadline. ARS Office/Lab and Location: A postdoctoral research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Stored Product Insect and Engineering Research Unit, Manhattan, KS Research Project: The U.S. Department of Agriculture - Agricultural Research Service (USDA ARS) mission involves problem-solving research in the widely diverse food and agricultural areas encompassing plant production and protection; animal production and protection; natural resources and sustainable agricultural systems; and nutrition; food safety; and quality. The programs are conducted in 46 of the 50 States, Puerto Rico, and the U.S. Virgin Islands. For ARS to maintain its standing as a premier scientific organization, major investments in computing, networking, and storage infrastructure are required. Training in data and information management are integral to the integrity, security, and accessibility of research findings, results, and outcomes within the ARS research enterprise. Nearly 2000 scientists and support staff conduct research within the ARS research enterprise. The SCINet/Big Data Research Participation Program of the USDA ARS offers research opportunities to motivated postdoctoral fellows interested in collaborating on agricultural-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 clusters (HPC). The objective of this fellowship is to facilitate cross-disciplinary, cross-location collaborative research on problems of interest to each applicant 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 a fellow to use the HPC to analyze large datasets. Under the guidance of Drs. Alison Gerken and Rob Morrison, the participant will develop and co-lead projects to understand the effects of climate change on stored product insect pests in the post-harvest supply chain. The Fellow will combine both empirical data collection and the use of public databases, including climate databases and species occurrence data, to develop and compare predictive models of insect pests and their distributions on both micro- and macro-scales. The Fellow will extend these models to machine learning and graphical user interface designs to inform post-harvest insect pest management decisions. In addition, the Fellow will apply ecological principles such as niche overlap, thermal reaction norms, spatial distribution and variability, and life history modeling to develop predictive programs for precision agriculture on post-harvest commodities. Models will be validated on both a laboratory and semi-field scale at the extensive resources available at the Center for Grain and Animal Health Research (silos, pilot-scale elevators, simulated warehouses, climate-controlled chambers) and through collaborations with faculty and students at Kansas State University. Learning Objectives: The participant will learn HPC computing technologies and will help develop and co-lead ARS-wide workshops, resulting in a community of scientific practice on spatial variation and predictive modeling of population growth. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on geospatial changes in insect distributions over time on both a large and small scale, how different facets of agriculture after harvest are responding to climate change, and the economic impacts of sustainable post-harvest insect pest management resulting in collaborative scientific papers and research presentations. USDA-ARS Contact: If you have questions about the nature of the research please contact Alison Gerken ( 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 the mentor and 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. If you have additional questions about the application process please email and include the reference code for this opportunity.
The qualified candidate should have received a doctoral degree by the appointment start date in one of the relevant fields listed below. Preferred skills: - Experience working with statistical modeling systems in R, SAS, or GIS - Ability to communicate with stakeholders and/or the public - Experience in experimental design for laboratory and/or field assays involving insects - Experience gathering, organizing, and analyzing large datasets
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