Post Masters Research Associate - Materials Engineer

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

The Earth Systems Predictability and Resiliency Group provides operational intelligence and systems performance forecasting to enable decision support. We use this information to understand and predict how the coupling of human-Earth processes (e.g. climate, water, land use, and energy system interactions) control complex energy and environmental systems behavior. Our researchers use integrated approaches that include sensing and measurement, environmental forensics, high-performance computing, spatial and non-spatial statistics, spatiotemporal analysis models, GIS algorithms, machine learning and artificial intelligence methods, and geovisualization. This position will provide support to existing and emerging federally funded programs focused on characterization, monitoring, modeling and/or predicting complex earth, energy and environmental systems. This includes application areas of climate modeling, extreme events, human-earth interactions, environmental remediation, carbon sequestration, renewable generation, power systems operation and planning, grid resiliency, and small cities.

The Environmental Sensors team within the Earth System Predictability & Resiliency Group of ESSD is seeking candidates to lead key tasks and support interdisciplinary projects including:

1) developing advanced sensing systems for earth system science and renewable energy. These next-generation sensors are not only fast, accurate, and versatile in terms of system performance but also lightweight, small, and soft. They can also be adapted for challenging environments such as deep subsurface and the Arctic environments, as well as for chemical detection.

2) studying the environmental impact of conventional hydropower and marine and hydrokinetic renewable energy systems by detecting and tracking fish or other animals in the river or ocean using underwater acoustic telemetry.

The primary scope of this position includes advancing sensor development, data processing and analysis, developing and optimizing materials mixing process using experimental fluid mechanics and acoustics, generation of intellectual properties, and writing of high-impact papers.

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:

• Candidates received a master’s degree in engineering with solid understanding of principles and concepts of materials science.
• Familiar with data visualization approaches, machine learning in Python (Pytorch, Weights & Biases, Huggingface), high performance computing
• Ability to effectively work and communicate within a multidisciplinary development team environment.
• Candidates must have received a master’s degree in engineering, material science or related field, or within the next 8 months from an accredited college or university.