Postdoctoral Researcher

The researcher will join the AmeriFlux Management Project, which supports a network of eddy covariance sites measuring carbon, water, and energy exchanges between ecosystems and the atmosphere. Key responsibilities include coordinating workshops, seminars, and community events, and collaborating with the AmeriFlux data team to develop AI-enabled tools, data products, and services. The position also provides opportunities to conduct independent research using AmeriFlux’s extensive datasets.

Working alongside ecosystem scientists, data scientists, and software engineers, the successful candidate will collaborate with a broad research community. Trevor Keenan will serve as the primary advisor. This position is ideal for someone passionate about integrating AI and environmental science, interdisciplinary collaboration, and advancing open science.

You will:

• Coordinate AmeriFlux Year of AI activities, including workshops, seminars, webinars, and community events.
• Collaborate with the AmeriFlux data team to develop AI/ML-enabled data products, tools, and workflows.
• Engage with site teams, data users, and AI/ML researchers to identify network needs and opportunities.
• Conduct independent research integrating AI/ML and flux observations to advance ecosystem science.
• Communicate findings through publications, presentations, reports, newsletters, and web content.

Additional Responsibilities as needed:

• Collaborate with related efforts at Berkeley Lab and beyond, including FLUXNET and other flux networks, remote sensing, and Earth system modeling.
• Contribute to the development and evaluation of AI tools, such as AI assistants, agents, or foundation models, that improve access to and use of flux data.
• Help develop community resources, such as benchmark datasets, best-practice guidance, or training materials, for AI applications in ecosystem science.

We are looking for:

• Recent Ph.D. in ecology, Earth or environmental science, atmospheric science, data science, computer science, or a related field.
• Demonstrated experience applying machine learning, statistical, or other data-driven methods to scientific data.
• Experience working with large, complex datasets, such as environmental time series, remote sensing, or model output.
• Strong communication and organizational skills, including the ability to engage with diverse scientific audiences.
• A record of scientific productivity, demonstrated through publications or other research outputs.
• Ability to work independently and collaboratively as part of a multidisciplinary, distributed team.

Desired skills/knowledge:

• Familiarity with eddy covariance data, flux networks (e.g., AmeriFlux, FLUXNET), or ecosystem carbon, water, and energy cycles.
• Proficiency in Python, R, or similar languages, and experience with reproducible research software practices (e.g., version control, open-source development).
• Experience with modern AI approaches, such as deep learning, foundation models, large language models, or AI agents.
• Participation in, or experience organizing, workshops, seminars, or other scientific community activities.
• Interest in open science, data stewardship, and FAIR data principles.