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Computational Materials Science - Postdoctoral Researcher

Entry Level | Full-time
Postdoctoral/Fellowship | livermore, CA | 06/20/2022

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Company Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is essential to innovation and creativity for continued success of the Laboratory’s mission.

Job Description

We have an opening for a Computational Materials Science Postdoctoral Researcher to participate in research dedicated to the development of high-temperature metal alloys and functional oxides. You will be part of a team focused on uncovering the origins of the exceptional mechanical properties of high entropy alloys (HEAs) as functions of composition and processing as well as discovering/mitigating the key material degradation mechanisms in high-temperature solid oxide electrolysis cells (SOECs) for efficient hydrogen generation. To tackle these multi-scale challenges, you will be a creative force in the development of at least two of the following integrated computational materials engineering (ICME) domains: precipitation and microstructure evolution simulations; thermal modeling and fluid dynamics simulations; phase stability calculations; mechanical property predictions; uncertainty quantification/propagation methods; and numerical optimization methods. This position is in the Actinides and Lanthanide Science group within the Materials Science Division.

In this role you will 

  • Propose, implement, and test new CALPHAD-based models and databases.
  • Develop, verify, validate, and maintain microstructure evolution simulation codes (PFC/PF, CAFE) for HEAs on LLNL supercomputers.
  • Develop and apply mesoscale (PF) models for microstructure degradation in SOECs; characterize degradation mechanisms and predict degradation time scales; work with other national labs to incorporate 3D microstructural data into mesoscale simulations and integrate results into larger-scale electrochemical performance prediction models.
  • Conduct computational analysis in a multi-physics massively parallel simulation code and support the thermal and hydrodynamics modeling of additive manufacturing processes such as direct energy laser deposition and generate material microstructures using the CAFE method.
  • Develop uncertainty quantification methods (including experimental and DFT data) and its propagation into ICME approaches for alloy design using numerical optimization methods.
  • Interface with experimentalists to validate models and propose directions for both structural and functional alloys property optimization.
  • 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/or technical meetings.
  • Perform other duties as assigned.


  • PhD in Materials Science, Condensed Matter Physics, Engineering, Computational Science, Applied Math, or a closely related field.
  • Experience and knowledge in at least two of the following areas: DFT, PF/PFC, precipitation modeling, CFD, CALPHAD software and database development, UQ/UP, ML, numerical optimization, and alloy mechanical property prediction applied to additive manufacturing and/or alloy design.
  • Ability to independently develop massively parallelized codes using Fortran, C/C++, Python, and advanced numerical solvers.
  • Knowledge of alloy metallurgy or functional oxides, including thermodynamics, phase stability, phase transformations, defect structures, and kinetics.
  • 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 conducting computational analysis in a multi-physics context and with software on massively parallel systems and/or in the development and use of long timescale implicit thermo-mechanical analysis software.
  • Experience in the design of algorithms, development of software, and their implementation and verification.
  • Experience with graded materials, high-entropy alloys and/or functional oxide materials for energy and fuel generating devices.

Additional Information

All your information will be kept confidential according to EEO guidelines.

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years.  Eligible candidates are those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

  • Included in 2022 Best Places to Work by Glassdoor!
  • Work for a premier innovative national Laboratory
  • Comprehensive Benefits Package
  • Flexible schedules (*depending on project needs)
  • Collaborative, creative, inclusive, and fun team environment

Learn more about our company, selection process, position types and security clearances by visiting our Career site

COVID-19 Vaccination Mandate

LLNL demonstrates its commitment to public safety by requiring that all new Laboratory employees be immunized against COVID-19 unless granted an accommodation under applicable state or federal law. This requirement will apply to all new hires including those who will be working on site, as well as those who will be teleworking.

Security Clearance

None required.  However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.  This process includes completing an online background investigation form and receiving approval of the background check.  (This process does not apply to foreign nationals.)  For additional information, please see DOE Order 472.2

Pre-Employment Drug Test

External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Equal Employment Opportunity

LLNL is an affirmative action and equal opportunity employer that values and hires a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.

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