USGS Dynamic Data Releases for Dynamic Data: Leveraging AI to Expedite Updates to a Range Shift Database

Organization U.S. Department of the Interior (DOI) Reference Code DOI-USGS-2026-11 How To Apply To submit your application, scroll to the bottom of this opportunity and click APPLY. A Complete Application Consists Of • An application • Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal institution systems may be submitted. Click here for detailed information about acceptable transcripts. • A current resume/CV, including academic history, employment history, relevant experiences, and publication list • Two educational or professional recommendations. At least one recommendation must be submitted in order for the mentor to view your application. All documents must be in English or include an official English translation. Connect with ORISE...on the GO! Download the new ORISE GO mobile app in the Apple App Store or Google Play Store to help you stay engaged, connected, and informed during your ORISE experience and beyond!” Description • Applications will be reviewed on a rolling-basis. USGS Office/Lab and Location: A research opportunity is currently available with the U.S. Geological Survey (USGS) located in Reston, Virginia. The USGS mission is to monitor, analyze, and predict current and evolving dynamics of complex human and natural Earth-system interactions and to deliver actionable intelligence at scales and timeframes relevant to decision makers. As the Nation's largest water, earth, and biological science and civilian mapping agency, USGS collects, monitors, analyzes, and provides science about natural resource conditions, issues, and problems. Research Project: Systematic literature reviews are a useful way to understand the state of knowledge on topics of interest. To remain useful, they need to be updated regularly as new information becomes available. The rapid pace of new publications makes it increasingly difficult to review all relevant papers and produce timely updates. Advances in AI/ML may be able to speed up reviews by screening out irrelevant articles and/or by extracting data from relevant papers potentially improving the efficiency and sustainability of USGS synthesis products. We will build on past USGS efforts to develop a transferable workflow using AI/ML to dynamically update reviews, using a systematic review on species range shifts as a case study (https://doi.org/10.1186/s13750-023-00296-0). From this review, we have a list of included and excluded articles, as well as the data we extracted from original research papers. We will look at what kinds of data AI can extract well, where it does not work well, and compare error rates to humans. Results will be shared via publications, open-source workflows, and presentations. Lessons learned will help USGS researchers apply AI/ML to diverse synthesis efforts to improve efficiency. You Will Have The Opportunity To • Create training and testing data from the existing range shift database; • Use AI tools to extract data from the articles included in the original review; • Compare AI results against human decisions, including performance metrics such as error rates, precision, and recall; • Document methods, tools, and evaluation results; • Contribute to publications, presentations, and workflow documentation as appropriate. Learning Objectives: Through this mentored research experience, you will gain hands-on training applying artificial intelligence and machine learning approaches to ecological data while contributing to one of the most comprehensive databases on species range shifts assembled to date. You will strengthen skills in data science, reproducible coding, and collaborative research, with the opportunity to contribute to a peer-reviewed manuscript and/or public data and code release as appropriate. Regular meetings with the Principal Investigator and the broader project team will provide structured mentoring, along with engagement in the Climate Adaptation Science Center (CASC) fellow community and its established mentoring resources. This experience supports professional development and networking with USGS, university, and nonprofit ecologists while advancing preparation for future research in climate adaptation science. Mentor: The mentor for this opportunity is Sarah Weiskopf (sweiskopf@usgs.gov). If you have questions about the nature of the research please contact the mentor(s). Anticipated Appointment Start Date: June 15, 2026. Start date is flexible and will depend on a variety of factors. Appointment Length: The appointment will initially be for 10 weeks, but may be renewed upon recommendation of DOI and is contingent on the availability of funds. Level of Participation: The appointment is full time. Participant Stipend: Stipend rates may vary based on numerous factors, including opportunity, location, education, and experience. If you are interviewed, you can inquire about the exact stipend rate at that time and if selected, your appointment offer will include the monthly stipend rate. Citizenship Requirements: This opportunity is available to U.S. citizens, Lawful Permanent Residents (LPR), and foreign nationals. Non-U.S. citizen applicants should refer to the Guidelines for Non-U.S. Citizens Details page of the program website for information about the valid immigration statuses that are acceptable for program participation. 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 USGS. Participants do not become employees of USGS, 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: If you have questions about the application process please email USGS@orau.org and include the reference code for this opportunity. Qualifications The qualified candidate should be currently pursuing or have received a bachelor's degree in the one of the relevant fields. Degree must have been received within the past four years, or anticipated to be received by 6/1/2029. Point of Contact Rachel Eligibility Requirements">12 ) • Degree: Bachelor's Degree or Master's Degree received within the last 48 months or anticipated to be received by 6/1/2029 12:00:00 AM. • Discipline(s): • Chemistry and Materials Sciences (