Postdoctoral Scholar

Lawrence Berkeley National Laboratory is seeking a Postdoctoral Scholar in the Earth & Environmental Sciences Area to study Gulf Coast coastal watershed variability using machine learning, AI, GIS, remote sensing, and geospatial and geophysical datasets. The researcher will develop AI-driven approaches, including image classification and explainable AI, to assess ecosystem vulnerability to disturbance and underlying geomorphological processes.

The position supports the Exploring Gulf Region Ecosystem Transitions (EGRET) project, an interdisciplinary collaboration investigating disturbance-driven ecosystem transitions and their ecological, hydrological, and geomorphological impacts. The postdoctoral scholar will collaborate with researchers across institutions, integrating AI/ML, remote sensing, process-based modeling, and field observations to advance predictive understanding of coastal ecosystem responses.

Candidates should have strong expertise in machine learning, coastal geomorphology, and geospatial analysis, along with excellent communication skills and a motivation to work in an interdisciplinary research environment.

We’re here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

Why join Berkeley Lab?
We invest in our postdoctoral scholars by offering benefits and resources designed to support your well-being, professional growth, and life outside of work:

• Comprehensive health benefits, including medical, dental, and vision coverage

• Retirement savings through the UC Defined Contribution Safe Harbor Plan, with additional voluntary UC retirement savings options available

• 24 days of Personal Time Off (PTO) per fiscal year, plus sick leave and paid holidays

• Eight weeks of Postdoctoral Paid Family Leave for qualifying family and parental needs

• Opportunities to connect, network, and grow through the Berkeley Lab Postdoc Association, including social events, professional development, and scientific exchange

• A collaborative and inclusive culture where you can grow your research career and belong

You will:

• Develop AI/ML methods for geospatial modeling and analysis of disturbance-driven coastal ecosystem transitions.

• Integrate and analyze geophysical, microbial, water quality, ecological, hydrological, and remote sensing datasets to understand ecosystem responses and geomorphological processes.

• Apply image classification, explainable AI, uncertainty quantification, and upscaling techniques.

• Collaborate with interdisciplinary researchers, including modelers and experimental scientists, to achieve project goals.

• Publish peer-reviewed articles and technical reports, present findings at conferences, and meet project deadlines.

• Maintain a safe, respectful, and collaborative research environment.

Additional Responsibilities as needed:

• Contribute to developing new field sites, apply new monitoring methods, collect new data during field campaigns, and interpret newly collected datasets from geophysical monitoring techniques including airborne EM, electrical resistivity tomography, and seismic surveys.

We are looking for:

• PhD in Geosciences, Earth and Environmental Sciences, Environmental Engineering, or a related field with a strong focus on geospatial analysis completed within the last 5 years.

• Demonstrated research productivity through oral and written skills, demonstrated through peer-reviewed publications and conference presentations.

• Ability to work independently and collaboratively as part of a large multidisciplinary team.

• Excellent interpersonal, oral, and written communication skills.

• Demonstrated experience developing and applying AI/ML models to enhance process understanding at watershed scales.

• Ability to collaborate with experimental microbiologists, hydrologists, and geochemists to improve predictive capabilities for hydro-biogeochemical processes;

• Develop uncertainty quantification tools and upscaling algorithms;

• Sufficiently knowledgeable on the use of high-performance computing, Python/R, parallel simulation and large dataset analysis/manipulation, and other coding tools

• Ability to identify and develop new research areas and contribute to research proposals.