Refine Search Clear All

Career Areas

Show More

Experience Level

Employment Type



Show More

Security Clearance

Keyword Search

Back to open positions

Computation-Focused High Temperature Alloy Design - Postdoctoral Researcher

Entry Level | Full-time
Postdoctoral/Fellowship | livermore, CA | 05/08/2021

Apply Now  

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 computation-focused Postdoctoral Researcher to work in the area of high-temperature alloy design and actively participate in research dedicated to uncovering the microstructural origins of the exceptional mechanical properties of high entropy alloys (HEAs) and discovering new refractory HEAs (RHEAs) for targeted high-temperature applications. You will be a creative force in the development and application of physics-based computational models for dislocation-mediated plasticity (e.g. phase-field / phase-field crystal) and physics- and data-based methods for mechanical property prediction (e.g. mechanistic yield stress models and machine learning models), and integrate these models / results into an alloy design framework that uses optimization methods to rapidly screen for promising compositions over the vast multicomponent HEA phase space. This position is in the Actinides and Lanthanides group within the Materials Science Division.

In this role you will

  • Propose, develop, and integrate solid-solution strengthening models (e.g. mechanistic / analytical and machine learning-based) for HEAs into LLNL’s alloy design framework (Materials Design Simulator).
  • Develop, verify, validate, and maintain a phase-field crystal simulation code for HEAs on LLNL supercomputers.
  • Understand and elucidate high-temperature strengthening mechanisms in RHEAs.
  • Generate and maintain a database of phase stability and mechanical properties for HEAs.
  • Interface with experimentalists to validate models and propose directions and guidance for RHEA 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 or technical journals and present results at external conferences, seminars, and/or technical meetings.
  • Perform other duties as assigned.


  • PhD in materials science, condensed matter physics, applied math, or a related field.
  • Experience and knowledge in at least two of the following areas: alloy mechanical property prediction, phase-field modeling, dislocation modeling, numerical optimization, HEA science.
  • Ability to independently develop massively parallelized codes using Fortran, C/C++, Python, and advanced numerical solvers.
  • Experience with numerical methods for solving partial differential equations such as finite difference, finite element, and/or spectral methods.
  • Knowledge of alloy metallurgy, including thermodynamics / phase stability, phase transformations, defect structure 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 in alloy design.
  • Experience with machine learning methods and/or uncertainty quantification and propagation.
  • Experience in crystal plasticity / solid mechanics constitutive modeling and/or density functional theory and/or molecular dynamics.

Additional Information

Why Lawrence Livermore National Laboratory?

  • Included in 2021 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

Security Clearance

LLNL is a Department of Energy (DOE) and National Nuclear Security Administration (NNSA) Laboratory.  Most positions will require a DOE L or Q clearance (please reference Security Clearance requirement).  If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing.  An L or Q clearance requires U.S. citizenship.  If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted.  For additional information please see DOE Order 472.2.

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.

Videos To Watch