Postdoctoral Researcher- COMPASS Project


1 August 2021 - 28 February 2022

Lawrence Berkeley National Lab’s (LBNL) Climate & Ecosystems Division has an opening for a postdoctoral scholar, characterizing ecosystem functioning and carbon and nutrient cycling across terrestrial-aquatic interfaces based on a suite of remote sensing data and data analytics (machine learning) tools.

Department of Energy’s Coastal Observations, Mechanisms, and Predictions Across Systems and Scales (COMPASS) project is a multi-institutional endeavor focused on understanding and predicting the transformations and fluxes of carbon and nutrients as they are exchanged across terrestrial-aquatic interfaces. Field- and lab-based understanding is developed in close integration with modeling needs and advances. Research spans both seawater- and freshwater-influenced coasts. The research emphasis is primarily on terrestrial processes that are influenced by coastal waters. This project includes several national labs, and research institutions in both regions, affording the successful candidate the opportunity for exciting and diverse collaborations.

In this exciting role, the postdoc will be responsible for coordinating and integrating large temporal and spatial datasets from public remote sensing databases as well as the datasets collected by a diverse set of field teams working on carbon and nutrient cycling as well as hydrology and biogeochemistry. The primary task is focused on big and diverse data analysis, integration and scaling using machine learning techniques, and characterizing the spatiotemporal dynamics of coastal ecosystems and biogeochemical functioning.

What You Will Do:

  • Work collaboratively with a diverse group of teams across multiple labs and universities under the COMPASS project to integrate their datasets for the spatiotemporal characterization
  • Analyze data and extract patterns and correlations from remote sensing and other environmental datasets, using appropriate data mining methods.
  • Develop and implement ML based algorithms for integrating spatiotemporal datasets including remote sensing and ground-based measurements for ecosystem and biogeochemical characterization
  • Author peer-reviewed conference or journal papers, and contribute to grant proposals.