Postdoctoral Researchers in Neural-Symbolic Methods for Scientific Computing

Los Alamos National Laboratory; Los Alamos (NM); Sur place; USD 90 000 - 110 000; Plein temps

Avantages offerts par ce poste

Dental and vision insurance

Free basic life and disability保险

Paid childbirth and parental leave

Learning opportunities and tuitionAss

401(k) with matching

Flexible schedules and time off

Onsite gyms and wellness programs

Relocation packages

Résumé du poste

Los Alamos National Laboratory in Los Alamos, NM, seeks postdoctoral researchers to develop neural-symbolic methods for automatically constructing and verifying numerical solvers for time-dependent PDEs. The work spans operator splitting, conservative schemes, and neural modeling, with rigorous stability and conservation checks.

The role combines numerical analysis, scientific ML, and large-scale computing, requiring strong PDE background, programming in Python/C++/Julia/Fortran, and strong

Qualifications

• PhD in applied/computational mathematics, computer science, computational physics, engineering, or related field completed within the last 5 years or near completion.
• Demonstrated research in numerical methods for PDEs and related computational science topics.
• Strong programming skills in Python, C++, Julia, or Fortran for scientific computing.
• Experience implementing, testing, and evaluating numerical algorithms beyond coursework.
• Publications or substantial projects in numerical analysis or scientific computing.
• Ability to work independently and collaboratively in a multidisciplinary setting.

Responsabilités

• Develop and analyze global and local operator-splitting algorithms for nonlinear PDEs, including shock-dominated systems.
• Build generative proposers for symbolic solver programs using autoregressive models and tree search.
• Develop numerical and symbolic verification tools for conservation laws and stability.
• Implement reliable research software and run numerical experiments on large-scale platforms.
• Design reproducible benchmarks comparing methods with classical solvers and ML baselines.
• Publish research and collaborate with LANL scientists and partner institutions.

Connaissances

Numerical methods

PDEs

Python

C++

Julia

Fortran

Communication skills

Independent work

Team collaboration

Scientific computing

Formation

PhD in applied/computational mathematics or related

Outils

Git

MPI

CUDA

GPU programming

Description du poste

What You Will Do

The Information Sciences Group (CAI-3) in the Computing and Artificial Intelligence Division at Los Alamos National Laboratory is recruiting postdoctoral researchers to develop neural-symbolic methods that automatically construct, verify, and adapt numerical solvers for time-dependent partial differential equations. The research will couple generative program synthesis with numerical and symbolic verification to produce explicit operator-splitting schemes with checkable measures of stability, accuracy, and conservation.

The Information Sciences Group (CAI-3) in the Computing and Artificial Intelligence Division at Los Alamos National Laboratory is recruiting postdoctoral researchers to develop neural-symbolic methods that automatically construct, verify, and adapt numerical solvers for time-dependent partial differential equations. The research will couple generative program synthesis with numerical and symbolic verification to produce explicit operator-splitting schemes with checkable measures of stability, accuracy, and conservation. The successful candidates will join a multidisciplinary team spanning numerical analysis, scientific machine learning, computational physics, dynamical systems, and large-scale scientific computing. The project will progress from smooth PDE systems to shock-dominated conservation laws and multiphysics problems, including compressible flow and magnetohydrodynamics. Methods will be tested on public scientific-machine-learning benchmarks and on computational kernels relevant to Los Alamos applications.

We are particularly interested in candidates with strong foundations in numerical methods for PDEs who want to work at the interface of scientific computing and machine learning. Depending on background, candidates may contribute to operator splitting, conservative and shock-capturing discretizations, generative models for symbolic programs, verifier-guided optimization, or formal certification of numerical algorithms. Experience in every area is not required; we value deep expertise in one area and a clear interest in learning across disciplines.

• develop and analyze global and spatially local operator-splitting algorithms for nonlinear PDEs, including shock-dominated and multiphysics systems;
• build generative proposers for symbolic solver programs using methods such as autoregressive models, discrete generative models, policy optimization, and tree search;
• develop numerical and symbolic verification tools for conservation laws, entropy consistency, stability, positivity, interface fluxes, and structural admissibility;
• implement reliable research software and run numerical experiments on large-scale computing platforms;
• design reproducible benchmarks that compare the proposed methods with classical numerical solvers, physics-informed models, neural operators, and related scientific-machine-learning baselines;
• publish original research, release research software when appropriate, and collaborate with scientists at Los Alamos and partner institutions.

What You Need

Minimum Job Requirements

• PhD in applied or computational mathematics, computer science, computational physics, engineering, or a related quantitative discipline, completed within the last 5 years or to be completed soon.
• Demonstrated research experience in numerical methods for PDEs, such as time integration, finite-volume, finite-difference, or finite-element methods, operator splitting, conservation laws, or computational fluid dynamics.
• Strong programming skills in one or more languages used for scientific computing, such as Python, C++, Julia, or Fortran.
• Experience implementing, testing, and evaluating numerical algorithms beyond coursework or classroom projects.
• Evidence of research accomplishment in numerical analysis, scientific computing, scientific machine learning, or a closely related field through publications, presentations, research software, or substantial projects.
• Ability to work independently and collaboratively in a multidisciplinary scientific environment.
• Strong interpersonal, written, and oral communication skills.

Desired Qualifications

• Experience with operator splitting, conservative or shock-capturing methods, adaptive or multirate time integration, magnetohydrodynamics, multiphysics, or multiscale simulation.
• Experience with scientific machine learning or generative modeling for structured or discrete objects, including reinforcement learning, Monte Carlo tree search, neural operators, or discrete-state generative models.
• Strong understanding of machine learning (ML) methods as evidenced through peer-reviewed publications, presentations, scientific software, or substantial research projects.
• Experience with formal methods or symbolic computation, such as satisfiability modulo theories, theorem proving, Lean, automated reasoning, or machine-checkable certificates for numerical algorithms.
• Basic Knowledge of numerical analysis, especially: aposteriori error analysis, adaptive control and conservation laws.
• Experience with high-performance computing, GPU or parallel programming, and collaborative software engineering practices, including testing, version control, and continuous integration.

Work Environment

Work Location: The work location for this position is onsite and located in Los Alamos, NM. All work locations are at the discretion of management.

Note to Applicants

• The appointment is for a duration of two years, with the possibility of a third-year extension based on performance evaluation.
• Candidates may be considered for a fellowship supported by the Center for Nonlinear Studies (CNLS). Outstanding candidates may be considered for a Director's Postdoc Fellowship or for the prestigious Richard P. Feynman, Darleane Christian Hoffman, J. Robert Oppenheimer, or Frederick Reines Distinguished Postdoc Fellowships.
• For more information about the Postdoc Program, please go to https://www.lanl.gov/engage/collaboration/postdoctoral-research
• Contact: Dr. Yen Ting Lin ([e-mail]) or Dr. Nishant Panda ([e-mail])
• Salary: Competitive salaries are based on the date the PhD degree requirements were completed, or the degree was awarded. Starting salary for a fresh PhD is currently $97,000. For more information, please refer to Postdoc Program website at https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php

Due to federal restrictions contained in the current National Defense Authorization Act, citizens of the People's Republic of China-including the special administrative regions of Hong Kong and Macau-as well as citizens of the Islamic Republic of Iran, the Democratic People's Republic of Korea (North Korea), and the Russian Federation, who are not Lawful Permanent Residents ("green card" holders) are prohibited from accessing facilities that support the mission, functions, and operations of national security laboratories and nuclear weapons production facilities, which includes Los Alamos National Laboratory.

Where You Will Work

• PPO or High Deductible medical insurance with the same large nationwide network
• Dental and vision insurance
• Free basic life and disability insurance
• Paid childbirth and parental leave
• Award-winning 401(k) (6% matching plus 3.5% annually)
• Learning opportunities and tuition assistance
• Flexible schedules and time off (PTO and holidays)
• Onsite gyms and wellness programs
• Extensive relocation packages (outside a 50 mile radius)

Additional Details

Directive 206.2 - Employment with Triad requires a favorable decision by NNSA indicating employee is suitable under NNSA Supplemental Directive 206.2. Please note that this requirement applies only to citizens of the United States. Foreign nationals are subject to a similar requirement under DOE Order 142.3A.

No Clearance

Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.

New-Employment Drug Test

The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing. Although New Mexico and other states have legalized the use of marijuana, use and possession of marijuana remain illegal under federal law. A positive drug test for marijuana will result in termination of employment, even if the use was pre-offer.

Internal Applicants

Regular appointment employees who have served the required period of continuous service in their current position are eligible to apply for posted jobs throughout the Laboratory. If an employee has not served the required period of continuous service, they may only apply for Laboratory jobs with the documented approval of their Division Leader. Please refer to Policy Policy P701 for applicant eligibility requirements.

Equal Opportunity

Los Alamos National Laboratory is an equal opportunity employer. All employment practices are based on qualification and merit, without regard to protected categories such as race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal, state, and local laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to [e-mail] or call (505)-664-6947.