Ross Davison
- @rosco
- | He/Him
NSF Postdoctoral Entrepreneurial Research Fellowship (I-PERF)
About the Role:
You will be a Postdoc on an NSF-funded, early-stage team focused on ecology and vegetation identification. This role leads R&D Development on generalizing Computer Vision models for vegetation identification across space/time/phenotypes. Where relevant, you will incorporate cutting-edge Foundation Models, such as Large Language Models (LLMs), into your work. There will be opportunities to publish research conducted during the position term and collaborate with faculty at UC Berkeley and Stanford.
Who we are:
Understory is an AI powered platform that provides geospatial tracking and verification for nature. Our data-driven roadmaps help people manage land, preserve biodiversity, and tackle climate change more effectively. We’ve grown into a diverse team of scientists, ecologists, and engineers revolutionizing our ability to monitor and protect natural resources for the communities that rely on them. Our goal is to radically improve environmental outcomes while removing financial barriers. We strive to tackle groundbreaking research problems with the efficiency and ingenuity of a startup.
How we do it:
Understory’s mission is to fight climate change and biodiversity loss by providing actionable metrics for environmental management and trusted verification for nature based solutions. We use a combination of drone and satellite imagery to identify vegetation down to the species level and quantify biological data. The resulting metrics provide measurable environmental performance indicators over the lifespan of an intervention or initiative. These data allow our customers to assess compliance for environmental regulation, verify sequestration in voluntary carbon markets, and improve outcomes from nature based solutions.
How you will contribute:
- Reviewing methods such as Large Language Models, Databases, Knowledge Graphs, and other methods of representing plant knowledge and querying it.
- Establishing the scope of vegetation-related and regional metadata that is useful in generalizing ML models across different conservation sites.
- Exploring different model architectures and feature designs for training ML models across different conservation sites.
About the NSF I-Perf Program
IPERF's focus is on candidates who are early-stage postdoctoral scholars. Women, underrepresented minorities in the sciences & engineering, or veterans of the U.S. Armed Forces are strongly encouraged to apply. Fellows serve a one-year research appointment within a small business research enterprise funded by NSF.
Compensation
The base salary range for this full-time position is $80,000 + benefits.
How to Apply
Please submit a brief cover letter, CV, and published publications to [email protected]