CASC MFEM Computational Scientist

Company Description

Join us and make your mark on the world! Lawrence Livermore National Laboratory (LLNL) strengthens the United States’ security by creating solutions for BIG ideas that improve our planet. We foster a culture that values diverse talent, partnerships, and innovative problem‑solving.

Salary: $202,500 – $256,824 yearly (SES 4 level). Pay is based on experience, education, certifications, performance, and organizational needs.

We are hiring a Computational Scientist to develop advanced finite element (FEM) meshing, discretization, and solver algorithms— including automatic differentiation—for high‑performance computing (HPC) applications based on the MFEM finite element library. The focus is on novel algorithms that benefit DOE’s large‑scale applications on GPU systems such as LLNL’s El Capitan. This position is part of the MFEM team in the Numerical Analysis & Simulations Group, CASC Division.

• Develop new numerical algorithms for PDEs to support efficient, scalable finite element simulations.
• Implement and test methods in the MFEM library, ensuring a flexible interface, parallel scalability, and GPU efficiency.
• Serve as a technical expert on a multidisciplinary team and collaborate with internal and external scientists.
• Act as the primary technical contact on projects, providing guidance to simulation tool users.
• Analyze complex problems, prioritize tasks, and creatively apply established methods.
• Troubleshoot issues and coordinate with team members and technical staff across organizations.
• Represent CASC in advanced computing discussions with vendors, customers, and academia.

• Ph.D. in Applied Mathematics, Computational Science, or equivalent combination of education and experience.
• Extensive experience with high‑order finite element discretizations, matrix‑free methods, solvers, and mesh adaptivity.
• Proficiency in C/C++ on Linux, MPI parallelism, and GPU acceleration (CUDA or HIP).
• Ability to work independently and manage concurrent technical tasks with competing priorities.
• Strong analytical, problem‑solving, and decision‑making skills.
• Excellent verbal and written communication; ability to collaborate with internal and external teams.

• Familiarity with the MFEM library and its large‑scale applications.
• Experience applying numerical methods in complex scientific applications.
• Knowledge of automatic differentiation tools such as Enzyme.
• Experience with ALE radiation‑hydrodynamics algorithms or similar physics.

#LI-Hybrid. No security clearance is required. Candidates must comply with NDAA Section 3112 restrictions on citizenship.

We are an equal‑opportunity employer committed to a work environment free of discrimination and harassment. All qualified applicants receive consideration without regard to protected characteristics.