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Post Doctoral Research Associate – AI for Building Energy Systems

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 Electricity Infrastructure and Buildings Division, part of the Energy and Environment Directorate, is accelerating the transition to an efficient, resilient, and secure energy system through basic and applied research. We leverage a strong technical foundation in power and energy systems and advanced data analytics to drive innovation, transform markets, and shape energy policy.

Responsibilities

At Pacific Northwest National Laboratory (PNNL), we are solving some of the nation’s greatest energy challenges by delivering transformative research and scalable innovations.

This position is in the Building Simulation and Design Group (BS&DG) within PNNL’s Electricity Infrastructure and Buildings Division. BS&DG conducts modeling and analysis to evaluate the impacts of building energy policies, codes, and standards; develops tools and workflows to support building research and decision making; and helps accelerate adoption of energy efficient technologies. The group maintains core research capabilities in building energy simulation, building energy policy analysis, and tool development for building applications.

BS&DG is seeking a Postdoctoral Research Associate – AI for Building Energy Systems. The successful candidate will be accountable to Project and/or Task Managers for performing assigned roles, following applicable project and field procedures, and completing assigned tasks on time and within budget. The candidate will also be accountable to the Group Leader and Team Leader for staff performance and development, operational discipline, and project execution.

This position is based in Portland, OR, Richland, WA, or Seattle, WA and requires an onsite presence. Hybrid work arrangements may be available in accordance with laboratory policy, project needs, and team expectations.

This role will support AI-enabled building energy systems research under DOE mission areas, with applications that may include building energy modeling, code compliance checking, permitting, large-scale performance data analysis, workflow automation, building controls and operations, workforce training, data mining, and AI-enabled software tools.

The successful candidate will join multi-disciplinary project teams and contribute to research on AI methods, computational methods, and technical workflows for building research, analysis, and decision making, while growing toward increased technical independence.

Conduct research in AI-enabled building energy systems research under a given mission area, including generative AI, large language models, and agentic systems for building research applications.
Develop and apply generative AI, large language model, and agentic methods for building research applications, including support for data preparation, testing, and technical evaluation.
Develop and support analysis methods, modeling workflows, tools, and prototypes for building research applications under guidance from project leads.
Contribute to technical products, analyses, and research outputs that support building research and decision making.
Contribute to existing building research and engineering projects in these application areas, including projects with established workflows and projects transitioning toward AI-enabled approaches.
Execute assigned technical tasks and deliver high-quality work within defined scope, schedule, and project expectations.
Work with project teams and collaborators to support technical tasks and respond to project needs.
Work effectively within interdisciplinary teams and collaborate closely with mentors, project leads, and teammates.
Support proposal development through technical analysis, literature review, or background research.
Contribute to technical reports, publications, and presentations related to assigned research.
Stay current with relevant literature, methods, and tools in support of assigned research and professional growth.

Applicants are strongly encouraged to submit a full academic or research CV.

Qualifications
Minimum Qualifications:

Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.

Preferred Qualifications:

PhD in architectural engineering, building science, mechanical engineering, or a closely related field, with strong potential to conduct independent research.

Experience conducting building energy system analyses, such as modeling, controls analysis, or system performance evaluation.
Experience developing or experimenting with AI or machine learning methods in engineering or scientific research applications.
Experience programming in Python or similar languages to support modeling, simulation, or research data analysis.
Experience contributing to research projects and delivering technical work within defined project scope and schedule.
Experience supporting proposal preparation, such as technical analysis or background research.
Record of technical publications or research contributions from doctoral research.
Experience communicating research results through presentations or publications.