Postdoctoral Research Associate in Environmental Studies: Modeling Agroforestry Transitions in New England
Dartmouth College | |
United States, New Hampshire, Hanover | |
Oct 30, 2024 | |
Dartmouth College: School of Arts & Sciences: Interdisciplinary Studies: Environmental Studies Location Hanover, NH Open Date Aug 13, 2024 Description The Ong Agroecology Lab in the Department of Environmental Studies (ENVS) at Dartmouth College invites applications for a postdoctoral researcher specializing in spatial ecology and theory of agroecosystems. The Ong Agroecology lab studies agroecosystems as complex systems, applying techniques from theoretical ecology and complex systems to understand and motivate transitions to sustainable food systems. This position is part of a USDA-funded project focusing on promoting climate-smart and sustainable agriculture in New England through regionally adapted agroforestry systems (Project "ADAPT"). The ADAPT project integrates research, education, and extension to (i) develop and test three agroforestry systems (silvopasture, polycultures (i.e. mulitstrata pererennial systems or food forests), forest farming), (ii) promote agroforestry training and outreach programs for landowners, professionals, and other interested communities through university extension, and (iii) build knowledge and skills around agroforestry as a climate smart solution for expanding food production through diverse educational opportunities. Project collaborators include researchers and extension staff from three universities (Dartmouth, University of New Hampshire, Yale University), partners from numerous institutions and organizations engaged in agroforestry-related activities (e.g., USDA National Agroforestry Center, USDA Forest Service Northern Research Station & Climate Hub, The Nature Conservancy, Whole Systems Design Collective, MidCoast Permaculture Design, ReTreeUS, Interlace Commons, Food Solutions New England) and individual collaborators throughout the region. Project results will contribute to developing climate-smart agriculture and forestry in New England through (i) mitigating greenhouse gas emissions, (ii) fostering market opportunities, and (ii) enhancing climate adaptation and resilience. We seek a highly motivated individual to develop and analyze mechanistic models of agroforestry transitions in the Northeast that optimize climate and socio-ecological benefits, while also considering potential tradeoffs by incorporating perspectives from diverse stakeholder and rightsholders groups. The person in this position will help integrate socio-ecological data collected across New England in agroforestry transition sites hosted at Dartmouth, partner institutions (University of New Hampshire and Yale University), and practitioner locations. The postdoctoral researcher will participate as part of a large interdisciplinary team of scientists and graduate students and will have substantial flexibility to develop their specific research focus within the context of the project team's expertise and the needs of the larger project. However, there is an expectation that the person in this position will develop mathematical and spatial models of agroforestry transitions in New England that integrates knowledge across several relevant fields (e.g., ecology, soil science, hydrology, social sciences, economics) to address complex research questions related to designing sustainable and climate-smart temperate agroforestry systems. This position is funded for 2 years starting Winter 2025, with flexible start before or after. This is a full-time, non-remote, in-residence position at Dartmouth College. The postdoctoral researcher will be primarily advised and hosted in the Ong Agroecology Lab. They will also be part of the Ecology, Evolution, Environment & Society (EEES) Program, a highly interactive and vibrant interdisciplinary community of over 100 faculty, graduate students, and post-docs. The EEES Program is committed to antiracism, equity, and inclusion, and resolves to create programs, measures, and systems of accountability that ensure trainee success. Applicants who self-identify as individuals from groups historically excluded from ecology and/or persons excluded because of their ethnicity or race (PEERs) are particularly encouraged to apply. Dartmouth is a research-intensive Ivy League university with graduate programs in the sciences, engineering, medicine, and business. Postdoctoral scholars are supported by the Guarini School for Graduate and Advanced Studies, including their diversity and inclusion initiatives. Dartmouth is committed to academic excellence and encourages the open exchange of ideas within a culture of mutual respect. People with different backgrounds, life experiences, and perspectives make the Dartmouth community diverse, which enhances academic excellence. In addition, we value applicants who have a demonstrated ability to contribute to Dartmouth's diversity initiatives in STEM research, such as the Women in Science Program, E. E. Just STEM Scholars Program, and Academic Summer Undergraduate Research Experience. Applicants should address in their cover letter how their research, teaching, service, and/or life experiences prepare them to advance Dartmouth's commitment to diversity in service of academic excellence. Qualifications
Preferred Qualifications:
Major Duties/Responsibilities:
Application Instructions Please submit the following materials electronically via Interfolio.
Review of applications will begin on November 22, 2024; applications submitted after this date will be reviewed until the position is filled. Recommendations letters will be requested for finalists only. For questions about the position, please contact Dr. Theresa W. Ong (theresa.w.ong@dartmouth.edu) with "ADAPT agroforestry modeling postdoc" in the subject line. Application Process This institution is using Interfolio's Faculty Search to conduct
this search. Applicants to this position receive a free Dossier account and can send all application materials, including confidential letters of recommendation, free of charge. Apply Now |