Post Doctorate RA - Chemical & Bio Processing

Pacific Northwest National Laboratory • Richland, WA

1d • Onsite

About The Position

The Energy and Environment Directorate at PNNL delivers science and technology solutions for the nation’s biggest energy and environmental challenges. The Energy Processes and Materials Division, part of this directorate, creates and delivers real-world solutions supporting the Department of Energy’s goals for national energy security. This division develops new technologies in areas such as energy storage, advanced materials manufacturing, applied catalysis, advanced separations, biomass conversions, carbon capture and utilization, and hydrogen production and storage. They employ a systems perspective that includes discovery, technology development, scale-up, and market acceptance issues for successful technology commercialization.

Requirements

• Received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.
• 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.

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.

Benefits

• health insurance
• dental insurance
• vision insurance
• robust telehealth care options
• several mental health benefits
• free wellness coaching
• health savings account
• flexible spending accounts
• basic life insurance
• disability insurance
• employee assistance program
• business travel insurance
• tuition assistance
• relocation
• backup childcare
• legal benefits
• supplemental parental bonding leave
• surrogacy and adoption assistance
• fertility support
• company-funded pension plan
• 401 (k) savings plan with company match
• 120 vacation hours per year
• ten paid holidays per year
• flexible work schedules

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree