Postdoctoral Associate
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Job no: 531147
Work type: Research Faculty
Senior management: College of Science
Department: Biological Sciences
Location: Blacksburg, Virginia
Categories: Research / Scientific
A new position for a postdoctoral research associate is available in the Center for Ecosystem Forecasting at Virginia Tech, co-supervised by Professors Quinn Thomas and Cayelan Carey. The Center is a vibrant, highly collaborative, interdisciplinary research team working on ecological forecasting at Virginia Tech. This onsite position will be at the nexus of many ongoing funded water quality forecasting projects and will provide hands-on training and many opportunities for communicating forecasts and research findings via peer-reviewed publications. Depending on the interests of the successful candidate, research activities could include: generating water quality forecasts with data from the Virginia Reservoirs LTREB (Long-Term Research in Environmental Biology; ltreb-reservoirs.org), NEON (National Ecological Observatory Network), and/or GLEON (Global Lake Ecological Observatory Network) and a suite of process and machine learning models; coupling catchment and lake models to build new forecasting workflows; integrating remote sensing data into lake and reservoir forecasts; developing software and/or cyberinfrastructure to support forecasting; and working closely to co-develop forecasts with a range of end users (including water managers, policy-makers, and community members). The position start date will be flexible, aiming for spring 2025 but later is also possible. Funding is available for at least two years, contingent upon a successful one-year review.
-We are seeking a Ph.D. in ecology, environmental science, or related field.
PhD awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining.
-The candidate must have quantitative research experience in ecological modeling/forecasting, ecosystem science, and/or data analysis
-The candidate must have experience working in the R programming language
-Experience working with ecosystem models for lakes and/or reservoirs
-Experience analyzing water quality data
-Substantial experience in R or equivalent programming languages (e.g., Python, Matlab)
-Experience with collaborative coding projects and using versioning control tools (e.g., Git)
-Experience in statistical analysis
-Experience in analyzing long-term and high-frequency sensor environmental data
-Experience in publishing peer-reviewed journal articles
-Demonstrated ability to work in interdisciplinary collaborative teams
Commensurate with experience
The successful candidate will be required to have a criminal conviction check.
About Virginia Tech
Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including the Innovation Campus in Northern Virginia; the Health Sciences and Technology Campus in Roanoke; sites in Newport News and Richmond; and numerous Extension offices and research centers. A leading global research institution, Virginia Tech conducts more than $500 million in research annually.
Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development.
Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.
If you are an individual with a disability and desire an accommodation, please contact Dreama Price at dreama@vt.edu during regular business hours at least 10 business days prior to the event.
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