Post Doctorate RA - Chemical & Bio Processing

About the role

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. 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, advancing renewable energy and efficiency technologies, and resolving complex issues in nuclear science, energy, and environmental management.

The Energy Processes and Materials Division, part of the Energy and Environment Directorate, creates and delivers real-world solutions that support the Department of Energy’s goals for national energy security. We deliver new technologies that connect fundamental science to applications in areas such as energy storage, advanced materials manufacturing, applied catalysis, advanced separations, biomass conversions, carbon capture and utilization, and hydrogen production and storage. We employ a systems perspective that includes discovery, technology development, and scale-up as well as economic, regulatory, and market acceptance issues necessary for successful technology commercialization.

Responsibilities

• Conduct independent research and contribute to multidisciplinary team assignments involving catalysis and renewable-fuels process development.
• Design, construct, modify, and safely operate laboratory-scale batch and continuous-flow reactor systems.
• Synthesize and characterize heterogeneous catalysts and evaluate their performance for reactions involving biomass-derived intermediates, waste-derived feedstocks, carbon dioxide, or other renewable carbon sources.
• Perform rigorous kinetic measurements and investigate reaction mechanisms, catalyst structure–performance relationships, and catalyst deactivation.
• Apply AI/ML methods to scientific and engineering problems, including catalyst formulation, reaction optimization, experimental selection, pathway discovery, and process modeling.
• Develop reproducible workflows and data pipelines that integrate catalyst synthesis, characterization, reaction testing, and product analytics.
• Apply approaches such as Bayesian optimization, active learning, uncertainty quantification, surrogate modeling, or digital twins to accelerate experimental research and support scale-up decisions.
• Analyze complex experimental datasets using statistical, computational, and mechanistic modeling methods.
• Collaborate with experimental researchers, computational chemists, data scientists, and external research partners.
• Prepare technical reports, peer-reviewed manuscripts, presentations, and other project deliverables.
• Present research results at technical conferences, sponsor meetings, and project or program reviews.
• Contribute to the development of research proposals, intellectual property, and new technical capabilities.
• Maintain a strong commitment to safe, rigorous, and reproducible laboratory research.

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:

• Ph.D. in Chemical Engineering, Chemistry, Physical Chemistry, Materials Science, Data Science, Computational Science, or a closely related field.
• Demonstrated experience conducting independent scientific or engineering research.
• Strong record of peer-reviewed publications and technical presentations.
• Excellent written and verbal communication skills.
• Ability to work effectively within multidisciplinary research teams.
• Experience with heterogeneous catalysis, catalyst synthesis, reaction engineering, or biomass conversion.
• Hands-on experience operating laboratory-scale batch or continuous-flow catalytic reactor systems.
• Experience measuring catalytic performance, reaction kinetics, selectivity, and catalyst stability.
• Familiarity with biomass-derived intermediates, oxygenates, biocrudes, waste-derived feedstocks, or catalytic upgrading pathways to fuels and chemicals.
• Experience applying AI/ML methods to chemistry, catalysis, materials science, or chemical engineering problems.
• Proficiency in Python or another programming language used for scientific data processing, modeling, and visualization.
• Experience with Bayesian optimization, active learning, machine learning, mechanistic modeling, uncertainty quantification, or automated experimentation.
• Experience developing reproducible data workflows or integrating experimental and computational datasets.
• Familiarity with spectroscopic or analytical techniques such as FTIR, NMR, GC, GC-MS, LC, or related methods.
• Knowledge of reaction kinetics, transport phenomena, catalyst deactivation, and reactor-scale behavior.
• Experience using large language models, agentic workflows, or generative AI to support literature analysis, hypothesis generation, data interpretation, or research automation.
• Demonstrated interest in translating fundamental scientific understanding into scalable and deployable chemical processes.

Benefits

• Medical, dental, and vision insurance, including robust telehealth care options and several mental health benefits.
• Free wellness coaching, health savings account, and flexible spending accounts.
• Basic life insurance and disability insurance.
• Employee assistance program and business travel insurance.
• Tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support.
• Company-funded pension plan and 401(k) savings plan with company match.
• Up to 120 vacation hours per year and ten paid holidays per year (Research Associates excluded).

All benefits are dependent upon eligibility.