AI-Enabled Catalyst Discovery Postdoctoral Researcher

Posted Yesterday
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Idaho Falls, ID, USA
In-Office
105K-105K Annually
Entry level
Energy • Cybersecurity • Defense • Industrial
The Role
Develops AI-enabled machine learning workflows for catalyst performance prediction and inverse catalyst design. Integrates experimental, kinetic, characterization, and reactor data into FAIR data pipelines and graph-based ontologies. Builds preprocessing, feature engineering, uncertainty quantification, validation, and candidate-ranking workflows for autonomous catalyst optimization. Collaborates with multidisciplinary experimental teams, validates models across development scales, and produces reproducible software, documentation, publications, and technical reports.
Summary Generated by Built In

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.

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. With more than 6,300 scientists, researchers, and support staff, the laboratory works with national and international governments, universities and industry partners to change the world's energy future and secure our nation's critical infrastructure.

INL Mission:

Our mission is to discover, demonstrate and secure innovative nuclear energy solutions, other clean energy options and critical infrastructure.

INL Vision:

Our vision is to change the world's energy future and secure our nation's critical infrastructure.

Selective Service Requirements:

To be eligible for employment at INL males born after December 31, 1959 must have registered with the Selective Service System (SSS). For more information see www.sss.gov.

Equal Employment Opportunity:

Idaho National Laboratory (INL) is an Equal Employment Opportunity (EEO) employer. It is the policy of INL to provide equal employment opportunities to all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information.

Reasonable Accommodation:

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Other Information:

When applying to positions please provide a resume and answer all questions on the following screens. Applicants, who fail to provide a resume or answer the questions, may be deemed ineligible for consideration.

INL does not accept resumes from third party vendors unsolicited.

Skills Required

  • PhD in Chemical Engineering, Materials Science, Computer Science, Data Science, Applied Mathematics, or a closely related field
  • PhD requirements completed by the commencement of appointment and within the previous five years
  • Experience developing machine learning models using Python and scientific computing libraries such as PyTorch, TensorFlow, or 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, materials 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
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The Company
6,300 Employees
Year Founded: 1949

What We Do

Idaho National Laboratory (INL) is a U.S. Department of Energy national laboratory and the nation’s leading center for nuclear-energy research and development. It conducts scientific and engineering work across integrated energy systems, clean-energy technologies, cybersecurity, national and homeland security, critical infrastructure, and environmental science. Its mission is to discover, demonstrate, and secure innovative nuclear-energy solutions, other clean-energy options, and critical infrastructure.

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