AI-Enabled Catalyst Discovery Postdoctoral Researcher

Primary

Responsibilities Include:

• Design, develop, and maintain machine learning workflows for catalyst performance prediction and inverse catalyst design.
• Develop forward predictive models that relate catalyst synthesis parameters, physiochemical characterization, and transient kinetic descriptors to catalytic performance metrics including yield, selectivity, and stability.
• Implement inverse-design algorithms that recommend new catalyst compositions and synthesis conditions for experimental validation.
• Integrate heterogeneous datasets generated from high-throughput synthesis, catalyst screening, transient kinetic measurements, and reactor scale testing into a unified data pipeline.
• Develop automated data preprocessing, feature engineering, uncertainty quantification, model validation, and candidate-ranking workflows.
• Interface machine learning models with the project’s graph-based ontology and FAIR data infrastructure to enable automated model training and data ingestion.
• Collaborate closely with catalyst synthesis, high-throughput screening, transient kinetics, and reactor testing teams to incorporate newly generated experimental data into iterative model refinement.
• Evaluate model performance using statistical cross-validation and experimental validation across catalyst development scales, from research powders through technical catalyst forms.
• Develop reproducible software, documentation, and visualization tools that support workflow deployment and long-term maintainability.
• Contribute to publications, technical reports, software releases, presentations, and project reviews.

• PhD in Chemical Engineering, Material Science, Computer Science, Data Science, Applied Mathematics, or a closely related field.
• PhD requirements must be completed by commencement of appointment and within the previous 5 years.
• Experience developing machine learning models using Python and scientific computing libraries (e.g., PyTorch, Tensor Flow, scikit-learn).
• Experience with scientific data analysis, statistical learning, and predictive modeling.
• Strong programming skills and experience with software version control.
• Demonstrated ability to work in multidisciplinary research teams.

• Experience applying machine learning to chemistry, catalysis, material science, or reaction engineering.
• Familiarity with Bayesian optimization, active learning, inverse design, or uncertainty quantification.
• Experience with graph databases, knowledge graphs, or ontology development.
• Experience developing scientific workflows for automated or high-throughput experimentation.
• Knowledge of heterogeneous catalysis, reaction kinetics, or catalyst characterization techniques.
• Experience with cloud computing, workflow orchestration, or containerized software environments.

While performing the duties of this classification, the employee is frequently required to stand, walk, sit, stoop, bend, and work in an office and laboratory environment. The job requires hand/finger dexterity to keyboard or type, handle materials, manipulate tools, and reach with hands and arms. The job requires operation of job-related equipment. The employee must occasionally lift and/or move up to 25 pounds without assistance.

Sufficient visual acuity and hearing capacity to perform the essential functions and interact with the people is required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• The pay for this position is $ Annually
  . At Idaho National Laboratory compensation decisions are determined using factors such as education, relevant experience, and other credentials.