You will develop and implement methodologies for monitoring and characterizing complex near-surface and surface ecosystem processes (e.g., plant traits/species/dynamics/evolution, soil biogeochemistry, hydrology) using remote sensing techniques (satellite/airborne/UAV), geophysics and ground-based measurements. You will work with a multi-disciplinary group of scientists to improve predictive understanding of coupled dynamics among hydrology, vegetation and biogeochemistry that are manifested from the plot to the watershed scales, and that is critical for understanding ecosystem functioning and carbon/nutrient cycling under climate change.
This role focuses on developing the methodology for (1) identifying co-variability among surface/subsurface processes, (2) integrating multiscale multi-type datasets, and (3) scaling critical ecosystem properties from point measurements to the watershed scale. The particular focus is on developing machine learning methods to fully integrate hyperspectral and LiDAR data for mapping plant traits across scales as well as on semi-automated identification of co-variability among datasets and their critical changes. The application areas include watershed science, water resources, and agriculture.
What You Will Do:
Process satellite/airborne/UAV-based multi/hyperspectral remote sensing, LiDAR-based remote sensing.
Analyze co-variability of remote sensing (e.g., optical images, LiDAR data for plants, topography) and subsurface measurements (e.g., geophysics, in situ soil sensors).
Develop new machine learning algorithms for extracting key information and understanding on plant-subsurface interactions from multi-type multiscale datasets, including remote sensing, geophysics, and soil/plant samples.
Lead interdisciplinary field campaigns including UAV, geophysics, and soil/plant sampling.
Identify and develop new research areas and proposals.
Engage as a member of a large, multidisciplinary research team that includes geochemists, hydrologists, and computational scientists.
Engage agricultural industry people and farmers for growing agro-ecosystem applications.
Author peer-reviewed journal articles and technical reports.
What Is Required:
Ph.D. in environmental engineering/science, ecology, electrical engineering, and computer science, or equivalent discipline; or equivalent work experience.
Familiarity with relevant software: GIS (e.g., ArcGIS, QGIS), remote sensing data processing (e.g., ENVI, Google Earth Engine), and machine learning (e.g., TensorFlow, R).
Familiarity with state-of-the-art field sensors and platforms such as RTK GPS, in situ soil moisture/temperature sensors, hyperspectral sensors, and UAVs.
Familiarity with frontier agroecology and watershed concepts.
Ability to identify and develop new research areas and proposals.
Ability to collaborate with a multidisciplinary team of scientists.
Ability to author peer-reviewed journal publications and technical reports.
Additional Desired Qualifications:
Proposal development experience (either success/fail).
Postdoctoral experience (>3 years).
Team science research experience.
International research exposure.
Requested Application Materials:
Curriculum Vitae and publication list.
Statement of research experience and interests.
List of three references (Names and contact information for at least three references (at least two external to the Lab and UCBerkeley)).
The posting shall remain open until the position is filled.
This is a full time, 2 year, career-track term appointment that may be renewed to a maximum of five years and that may be converted to career based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs.
Full-time, M-F, exempt (monthly paid) from overtime pay.
Salary is commensurate with experience.
This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.
Berkeley Lab (LBNL) addresses the world’s most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Lab’s scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energy’s Office of Science.