Postdoctoral Researcher in Adaptive Generative Modeling for Scientific Applications

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 87,000 - 107,000; Full time

Benefits offered by this job

Medical insurance

Dental and vision

Paid time off

Job summary

Los Alamos National Laboratory's Information Sciences Group (CAI-3) in the Computing and Artificial Intelligence Division seeks a postdoctoral researcher to advance modern machine learning methods for complex spatiotemporal physical-science problems, including diffusion and related generative models.

The role involves collaboration across mathematics, ML, statistics, computational physics, materials science, and accelerator physics.

Qualifications

• PhD in a theoretical or computational physics, applied mathematics, materials science, engineering, or related quantitative field.
• Strong track record in ML research and publications or substantial projects.
• Programming experience in Python or Julia and modern ML libraries.

Responsibilities

• Develop, implement, and evaluate ML and generative models for scientific data.
• Work with large experimental and simulation datasets from multiple physical-science domains.
• Develop high-quality research software for large-scale computing platforms.
• Publish original research in leading journals and present results at conferences.
• Collaborate with ML researchers and domain scientists on multidisciplinary projects.

Skills

PhD in theoretical/computational/phys

Machine learning methods

Python (PyTorch)

Collaboration skills

Education

PhD in a quantitative physical-science field

Tools

PyTorch

TensorFlow

Keras

JAX

Job description

What You Will Do

The Information Sciences Group (CAI-3) in the Computing and Artificial Intelligence Division at Los Alamos National Laboratory is recruiting a postdoctoral researcher to develop and apply modern machine learning methods to complex spatiotemporal problems in physical sciences. The research will include generative modeling, with particular interest in diffusion and related models for high-dimensional, time-varying systems.

The successful candidate will join a multidisciplinary research team spanning applied mathematics, machine learning, statistics, computational physics, materials science, and accelerator physics. The research will focus on learning high-dimensional, time-varying physical systems from sparse experimental and simulation data. Application areas include three-dimensional imaging and modeling of evolving materials, virtual diagnostics for charged-particle beams, and digital twins for scientific experiments and facilities.

We are particularly interested in candidates with strong foundations in applied mathematics, physics, or statistics, who have experience in machine learning and are motivated to work closely with domain scientists. Research involve deep learning, generative models, representation learning, inverse problems, uncertainty quantification, and methods for incorporating scientific constraints or real-time observations into learned models. Prior experience with diffusion models or the specific application areas is welcome but not required.

• develop, implement, and evaluate machine-learning and generative models for scientific data;


• work with large experimental and simulation datasets from multiple physical-science domains;


• develop high-quality research software for large-scale computing platforms;


• publish original research in leading scientific and machine learning journals and present results at conferences;


• contribute to a general machine learning framework for reliable digital twins of time-varying physical systems;


• collaborate with machine learning researchers, computational scientists, and experimental teams.

What You Need

Minimum Job Requirements:

• PhD in theoretical or computational physics, applied mathematics, materials science, engineering, or a related quantitative physical-science discipline, completed within the last 5 years or to be completed soon. Candidates with a PhD in applied mathematics, computer science, statistics, or a related field must demonstrate substantial research experience in the physical sciences.


• Strong understanding of machine learning methods as evidenced through peer-reviewed publications, presentations, scientific software, or substantial research projects.


• Excellent programming skills and demonstrated experience using high-level languages such as Python or Julia..


• Practical experience (outside of coursework or certifications) with modern ML tools and libraries, such as PyTorch, TensorFlow, Keras, or JAX.


• Experience applying machine learning to scientific, engineering, or complex quantitative data.


• Ability to work independently and collaboratively in a multidisciplinary scientific environment.


• Strong interpersonal, written, and oral communication skills.

Desired Qualifications:

• Experience, substantiated by peer-reviewed publications, in generative modeling including normalizing flows, diffusion models, flow matching, stochastic interpolants, or Schrödinger bridges.


• Experience with inverse problems, representation learning, uncertainty quantification, dynamical systems, or scientific machine learning.


• Experience with high-performance computing, GPU programming, distributed training, parallel programming, and related paradigms.


• Familiarity with software engineering, including testing, code version control, and continuous integration workflows.

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]) and Dr. Alex Scheinker ([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

Located in beautiful northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security.

Our generous benefits package includes:

• 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.