Computational Materials Scientist - Postdoctoral Researcher

Entry Level | Full-time
Postdoctoral/Fellowship | Livermore, CA | 10/22/2026
Reference #: REF
8458F
Job Code: PDS.
1 Post-Dr Research Staff 1
Organization: Physical and Life Sciences
Position Type: Post Doctoral
Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

We have an opening for a Postdoctoral Researcher to work in the field of computational materials science and actively participate in research dedicated to the discovery of new structural alloys for extreme environments. You will be involved in research to implement methods for materials design and process development. You will be a creative force in the integration and application of thermodynamic and kinetic models into an alloy design framework (including material properties models) that uses numerical optimization methods to rapidly screen for promising compositions over vast multi-component phase spaces.

This position is in the Actinide and Lanthanide Science group within the Materials Science Division.

In this role you will

• Independently develop multicomponent thermodynamic and kinetic databases for inorganic (metal, oxide, carbide, hydride, etc.) systems.
• Incorporate new Integrated Computational Materials Engineering (ICME) microstructure evolution models and resulting property predictions (ranging from analytical to machine learning surrogate models) in LLNL’s Materials Acceleration Platform that is deployed on LLNL’s High Performance Computing infrastructure.
• Develop uncertainty quantification (UQ) and propagation methods into ICME approaches.
• Interface with experimentalists in design of experiment and material development campaigns.
• Work both independently and collaborate with others in a multidisciplinary team environment to accomplish program goals.
• Publish research results in peer-reviewed scientific journals and present results at external conferences, seminars, and technical meetings.
• Perform other duties as assigned.

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• PhD in materials science, metallurgy, condensed matter physics, applied math, or a closely related field.
• Experience and knowledge in at least three of the following areas: CALPHAD, microstructure and property modeling, uncertainty quantification and propagation, ICME, alloy design, design of experiments.
• Demonstrated ability to independently develop or make significant contributions to scientific research software.
• Experience with commercial (Thermo-Calc, Pandat, or Fact Sage) or open source (PyCalphad, Thermochimica, or Open Calphad) computational thermodynamics software to perform CALPHAD database development.
• Experience in at least twoof the following metallurgy topics: thermodynamics, phase stability, phase transformations, defect structures, solidification, or thermo-mechanical processing.
• Proficient verbal and written communication skills as reflected in effective presentations at meetings and a demonstrated strong publication record.
• Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.

Qualifications We Desire

• Experience with Super Intelligence (SI)(formerly referred to as Artificial Intelligence, or AI) and/or machine learning (ML) methods.
• Experience with algorithms relevant for materials design and discovery (Bayesian Optimization, black-box optimization, gradient-based optimization).
• Experience parameterizing or developing numerical methods for thermodynamic and/or kinetic modeling of phase transformations and microstructure evolution (e.g. CALPHAD, Kampmann-Wagner numerical method, phase field, cellular automata, Monte Carlo method,…