Post Bachelors Research Associate - Biogeochemistry

Overview

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.  

Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. 

The Earth and Biological Sciences Directorate (EBSD) leads critical research in four areas: Atmospheric, Climate & Earth Sciences, Biological Sciences, Environmental Molecular Sciences, and Global Change. Our vision is to develop a predictive understanding of biological and Earth systems in transition. We aim to understand energy and material flows within the integrated Earth system; to understand, predict, and control the response of biosystems to environmental and/or genomic changes; and to   Model the Earth system from the subsurface to the atmosphere.  

The Biological Sciences Division has 17 collaborative, interdisciplinary biology-based teams to tackle major challenges in ecosystem sustainability, bioenergy, human health, and national security. Teams use unique field sites, advanced instrumentation, and integrated computational approaches to explore molecular-scale processes. Strategic efforts focus on advancing molecular measurement capabilities, uncovering the structure and function of molecular dark matter, identifying control points in complex systems, and developing domain-aware AI to accelerate discovery and hypothesis-driven research. 

Responsibilities

The Multiscale System Science Group is seeking a highly motivated post-bachelor’s research associate with a strong background centered around aquatic and/or terrestrial biogeochemistry.

The successful candidate will work with a large interdisciplinary team on execution of field and laboratory efforts. The candidate will generate new data as a part of PNNL’s river corridor science focus area (RCSFA) program (https://www.pnnl.gov/projects/river-corridor).

Responsibilities will include laboratory processing and field collection of many water and sediment samples, data management activities, including managing, running and assuring data quality from various analytical laboratory techniques. The candidate is expected to have good record keeping skills vital to maintain reproducibility of experiments and field tasks. This is a full-time position anticipated to last 6 months with a possible extension 

• Collect and analyze environmental samples from across diverse river corridor settings (soils, sediments, river water, vegetation).
• Maintain in-situ sensor equipment (YSI EXO sondes, rain gauges, etc).
• Conduct fieldwork across seasons including in snow/winter conditions.
• Conduct laboratory incubations of sediments, waters and pyrogenic organic matter.
• Analyze spatial datasets using QGIS and R.
• Conduct data analysis in R.
• Assist with manuscript and presentation preparations.

Qualifications

Minimum Qualifications:

• Candidates must have received a Bachelor’s degree within the past 24 months or within the next 8 months from an accredited college or university.  

Preferred Qualifications:

• Bachelor’s degree in environmental science, biogeochemistry, chemistry, or a related discipline.
• Experience conducting biogeochemical sample collection and hydrologic fieldwork in a variety of weather conditions.
• Experience with general laboratory activities such as preparing solutions, pipetting, and measuring small volumes and weights.
• Familiarity with basic biogeochemistry techniques such as carbon and nitrogen analyses.
• Strong interest in team-based, interdisciplinary science, with the ability to work independently when needed.
• Excellent organizational, written, and oral communication skills with a high level of attention to detail.
• Experience using scripting languages such as Python or R for data processing, analysis, and visualization.