Post Masters Research Associate – Waterpower Science & Ecology

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 Energy and Environment Directorate delivers science and technology solutions for the nation’s biggest energy and environmental challenges. Our more than 1,700 staff support the Department of Energy (DOE), delivering on key DOE mission areas including: modernizing our nation’s power grid to maintain a reliable, affordable, secure, and resilient electricity delivery infrastructure; research, development, validation, and effective utilization of renewable energy and efficiency technologies that improve the affordability, reliability, resiliency, and security of the American energy system; and resolving complex issues in nuclear science, energy, and environmental management.  

The Earth Systems Science Division, part of the Energy and Environment Directorate, provides leadership and solutions that advance Earth system opportunities for energy systems and national security. We are a multidisciplinary division connected by a shared commitment to innovate and collaborate towards solving complex problems in the dynamic Earth system.  

Responsibilities

Be part of a large interdisciplinary team researching the effects of hydropower operations on the passage, survival, and injury of juvenile salmon and lamprey across the Columbia, Snake, Willamette, and other river systems in the Pacific Northwest. Research activities include deploying, retrieving, maintaining, and testing acoustic and radio telemetry equipment; tagging fish or assisting with surgical implantation of transmitters; and transporting and releasing live, tagged fish according to detailed schedules.
Project responsibilities include fieldwork, data collection, data analysis, and report writing. Seasonal fieldwork may require frequent regional travel. The successful candidate may also support laboratory studies involving fish handling, maintaining fish populations in aquaculture facilities, and conducting detailed necropsies.
In addition, the position may involve conducting comprehensive fish injury assessments on specimens that have undergone turbine passage (simulated or actual) or transit through other water infrastructure facilities. These assessments include documenting internal and external injuries through established necropsy protocols to ensure accuracy and ethical handling. The role may also require analysis of high-resolution imagery to support precise injury quantification and contribute to data collection and model training for AI-based Fish Assessment tools aimed at improving predictive analytics and mitigation strategies in aquatic environments.

Qualifications

Minimum Qualifications:

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

Preferred Qualifications:

• Master’s degree in fisheries, environmental sciences, biology, or a related field, earned within the past two years.
• Exceptional written communication and presentation skills, with the ability to engage diverse audiences.
• Strong understanding of energy and natural resource issues.
• Outstanding organizational skills and attention to detail.
• Knowledge of telemetry study design and array maintenance.
• Demonstrated experience conducting field work.
• Hands-on experience handling and tagging live fish.
• Proficiency in managing large datasets and performing data analysis using tools such as MS Excel, R, SQL, or Python.
• Familiarity with mark‑recapture study designs, survival estimation methods, multiple regression modeling, and/or maximum likelihood estimation.
• Ability to work both independently and collaboratively, coupled with a strong work ethic.
• Willingness to work additional hours as needed to support project milestones and research objectives.