finished (2023/07/14, finished 2024/08/08, listed until 2024/01/14)The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and home to Cornell University's second-largest population of students, faculty, and staff. We work across disciplines to tackle the challenges of our time through world-renowned research, education, and outreach. The questions we probe and the answers we seek focus on three overlapping concerns: natural and human systems; food, energy, and environmental resources; and social, physical, and economic well-being. CALS is fundamentally invested in improving the lives of people, their environments, and their communities both in New York state and around the world.
Changing fire regimes in dry forest ecosystems in western North America are precipitating potentially massive losses in forest cover. In the mountains of Southern California – and in other arid ecosystems – biodiversity is concentrated in riparian corridors, so fires affecting these areas can have significant conservation implications. However, concentrating post-fire restoration in riparian areas can lead to significant returns. By combining passive acoustic surveys, the machine learning algorithm BirdNET (https://birdnet.cornell.edu), which generates avian diversity data from the audio (>100 species in the focal landscape), and single- and multi-species occupancy models, the incumbent will address urgent conservation challenges in regional and potentially global biodiversity hotspots. Core objectives of this project include: (1) the implementation of a passive acoustic survey effort to assess the effects of fire and efficacy of restoration in the Transverse Range of Southern California, and (2) using field data and simulations to develop a scalable and transferrable monitoring program that could be implemented at broad spatial scales.
The project scope provides an opportunity to make direct contributions to conservation and basic ecology. We will collaborate with federal agency biologists and managers (U.S. Forest Service), and academic researchers specializing in ecology, statistics, and computer science. Pending additional funding, the project may be expanded to build upon existing relationships with international NGOs such that the scope of the project entails generalized survey design for arid ecosystems.
In addition to working with external partners, the postdoctoral associate will be working closely with scientists in the Yang Center and other programs at the Lab. We encourage postdoctoral associates to explore related research questions and collaborate on projects conducted in the Lab and with partners.
This position is a one year-appointment, with a second-year extension with successful performance. Further extension is contingent upon funding.
Expected outcomes of this work are the submission of two manuscripts to high-impact peer-reviewed journals and coordination with agency partners to directly apply results to conservation in Southern California – and potentially multiple areas in Africa.
The incumbent will be expected to meet regularly with project collaborators (within the Yang Center, at other universities, and in government agencies). In addition, there are opportunities for the incumbent to mentor undergraduate students and possibly contribute to informal mentoring graduate students, but neither activity is expected or required.
Local personnel will have the flexibility to work on a hybrid schedule with opportunities to work on-site and from home. Fully remote work will be considered on a case-by-case basis.
Please apply via Academic Jobs Online https://academicjobsonline.org/ajo/jobs/25149.
Qualified candidates should submit a short cover letter, curriculum vitae, contact information for three references, and a statement of contribution to diversity, equity, and inclusion via the website.
Applications will be reviewed as received, continuing until a suitable applicant is identified.
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Pay Ranges:
The hiring rate of pay for the successful candidate will be determined considering the following criteria:
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