Idaho National Laboratory

AI-Enabled Catalyst Discovery Postdoctoral Researcher

Pharmaceuticals & Biotech
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • PhD in Chemical Engineering, Material Science, Computer Science, Data Science, Applied Mathematics, or closely related field.
  • PhD requirements must be completed by the commencement of appointment and within the previous 5 years.
  • Experience developing machine learning models using Python and scientific computing libraries.
  • Experience with scientific data analysis, statistical learning, and predictive modeling.
  • Strong programming skills and experience with software version control.

Responsibilities

  • Design, develop, and maintain machine learning workflows for catalyst performance prediction.
  • Develop predictive models relating catalyst synthesis parameters to catalytic performance metrics.
  • Implement inverse-design algorithms recommending new catalyst compositions for experimental validation.
  • Integrate heterogeneous datasets into a unified data pipeline.
  • Develop automated workflows for data preprocessing, feature engineering, and uncertainty quantification.
  • Interface machine learning models with the project’s graph-based ontology for automated model training.
  • Collaborate with interdisciplinary teams to incorporate experimental data into model refinement.

Benefits

  • Medical, Dental, Vision, and Flexible Spending Accounts
  • 401(k) with a 4.2% employer contribution and up to 4.8% match
  • Paid time off (personal leave)
  • Employee Education Program (tuition assistance for eligible positions)
  • Comprehensive Relocation Package
Full Job Description
Job Description

Idaho National Laboratory is hiring a postdoctoral researcher in Chemical Engineering, Materials Science, Computer Science, Data Science, Applied Mathematics or closely related field to support our Integrated Energy Technologies Department. The postdoctoral researcher will lead the development of an artificial intelligence (AI)-enabled catalyst discovery workflow that integrates experimental data, mechanistic understanding, and machine learning to accelerate heterogeneous catalyst development for propane dehydrogenation. This position lies at the interface of catalysis, data science, and scientific software development, supporting the creation of a closed loop experimental and computational platform for autonomous catalyst optimization.

Our team works a 9x80 schedule located out of our Idaho Falls facility with every other Friday off.

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.

Required:
  • 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, TensorFlow, 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.

The ideal candidate will possess:
  • 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.

Physical Requirements:

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.

Job Information:
  • The pay for this position is $105,144.00 Annually. At Idaho National Laboratory compensation decisions are determined using factors such as education, relevant experience, and other credentials.


About Us

Benefits and Relocation

  • Medical, Dental, Vision, and Flexible Spending Accounts
  • 401(k) with a 4.2% employer contribution and up to 4.8% match (regular positions) or self-contribute access (postdoctoral positions)
  • Paid time off (personal leave)
  • Employee Education Program (tuition assistance for eligible positions)
  • Comprehensive Relocation Package
  • Benefit eligibility subject to multiple factors, including employment status and position classification.


At this time, BEA will not sponsor any H1-B visas obtained outside of the United States of America (U.S.A.), including consular visas.

About Idaho National Laboratory

Idaho National Laboratory (INL) is a science-based, applied engineering national laboratory dedicated to supporting the U.S. Department of Energy's mission in nuclear energy research, science, and national defense. INL is operated by Battelle Energy Alliance, LLC, a partnership between Battelle, BWX Technologies, Inc., and Texas A&M University System. INL's work is focused on delivering solutions to some of the nation's most pressing challenges in energy, national security, science, and the environment. INL's research and development capabilities are organized into four primary areas: Nuclear Science and Technology, National and Homeland Security, Energy and Environment, and Biological Science and Engineering.
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