Post Master's Research Associate - Scientific Data Exploration

• Data Preparation & Analysis: Aggregate, transform, and integrate structured and unstructured datasets and documents, such as equipment datasheets, from environmental and power grid systems domains into a central, structured database.
• Power Grid Systems: Knowledge of environmental and power grid systems, with experience analyzing and organizing power grid data from a variety of external sources.
• Generative AI & Foundation Models: Development and evaluation of large-scale generative AI workflows for material science applications, such as atomic-resolution and hyperspectral data analysis, using multi-agentic systems, tool use, and optimizations for robustness, alignment, and efficiency.
• Agentic AI Systems & Autonomous Workflows: Design and deploy scalable agentic AI systems with autonomous workflows, dynamic tool use, and complex decision-making to support advanced scientific and operational tasks.
• Data Engineering & Pipeline Architecture: Design of scalable data systems, including data meshes and knowledge graph-enabled pipelines, to support large-scale scientific and production workflows involving scientific instrumental images, metadata, and attribute-rich data.
• Scientific Retrieval & DataMesh Systems: Development and evaluation of DataMesh-style scientific retrieval systems that combine embedding-based vector databases, knowledge graphs, and attribute extraction to enable content-based search and analysis over instrumental images and associated metadata.

• 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., * Masters degree in Computer Science, Data Science, Applied Mathematics, Electrical Engineering, Computational Engineering, or a closely related field.Prior research experience, including internship experience within a national laboratory setting.
• Experience developing or experimenting with agentic systems or machine learning methods in engineering or scientific research applications.
• Experience with developing data pipelines to extract and aggregate relevant information from multiple sources.
• Strong programming experience in C++, and/or Python.
• Strong written and verbal communication skills.
• Experience working in collaborative and interdisciplinary research environments.

Hazardous Working Conditions/Environment

PNNL lists the full pay range for the position in the job posting. Starting pay is calculated from the minimum of the pay range and actual placement in the range is determined based on an individual's relevant job-related skills, qualifications, and experience. This approach is applicable to all positions, with the exception of positions governed by collective bargaining agreements and certain limited-term positions which have specific pay rules.

As part of our commitment to fair compensation practices, we do not ask for or consider current or past salaries in making compensation offers at hire. Instead, our compensation offers are determined by the specific requirements of the position, prevailing market trends, applicable collective bargaining agreements, pay equity for the position type, and individual qualifications and skills relevant to the performance of the position.

Minimum Salary

USD $80,500.00/Yr.

Maximum Salary

USD $104,700.00/Yr.

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 Physical and Computational Sciences Directorate's (PCSD's) strengths in experimental, computational, and theoretical chemistry and materials science, together with our advanced computing, applied mathematics and data science capabilities, are central to the discovery mission we embrace at PNNL. But our most important resource is our people-experts across the range of scientific disciplines who team together to take on the biggest scientific challenges of our time. The Advanced Computing, Mathematics, and Data Division (ACMDD) focuses on basic and applied computing research encompassing artificial intelligence, applied mathematics, computing technologies, and data and computational engineering. Our scientists and engineers apply end-to-end co-design principles to advance future energy-efficient computing systems and design the next generation of algorithms to analyze, model, understand, and control the behavior of complex systems in science, energy, and national security., Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them! At PNNL, you will find an exciting research environment and excellent benefits including health insurance, and flexible work schedules. PNNL is located in eastern Washington State-the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab's campus is only a 45-minute flight (or ~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs., Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a "country of risk" without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries, you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.