Postdoctoral Research Associate: AI/ML-driven computational materials science and chemistry of f-elements

Los Alamos National Laboratory’s XCP-AI4ND and T-1 groups are seeking several highly motivated postdoctoral researchers to join a dynamic multi-disciplinary team focused on integrated molecular simulation, molecular design, and optimal experiment design for f-element materials. The successful candidate will work at the intersection of quantum chemistry, machine learning, and agentic-AI applied to f-element chemistry, including critical materials and lanthanides/actinides separation. The position offers exceptional opportunities for cross-disciplinary collaborations between theoretical and experimental chemists/physicists and data/computational scientists.

Responsibilities

• Develop and apply machine learning, data science, uncertainty quantification, and surrogate modeling to molecular or condensed-phase systems.
• Create AI/ML scientific workflows for computational chemistry or materials science.
• Utilize first-principle electronic structure methods such as density functional theory.
• Perform atomistic simulations of molecular and/or condensed-phase systems, including molecular dynamics, Monte Carlo, and free-energy calculations.
• Work creatively and independently while collaborating with experts in multi-disciplinary teams.

Requirements

• Broad experience with machine learning, data science, uncertainty quantification, and surrogate modeling applied to molecular or condensed-phase systems.
• Demonstrated experience developing AI/ML scientific workflows for computational chemistry or materials science.
• Strong background in first-principle electronic structure methods such as density functional theory.
• Strong experience with atomistic simulation of molecular and/or condensed-phase systems, including molecular dynamics, Monte Carlo, and free-energy calculations.
• Excellent oral and written communication skills.
• Ability to work creatively and independently as well as collaborate in multi-disciplinary teams.

Qualifications

• Ph.D. in Chemistry, Physics, Materials Science, Chemical Engineering, Applied Mathematics, or a related field completed within the last five years.

Skills

Desired skills (preference given to candidates with at least one):

• Experience with separation science, coordination chemistry, and f-element chemistry.
• Development or application of machine-learned interatomic potentials.
• AI/ML-guided approaches to molecular and materials design.
• Optimal design of experiment/Bayesian optimization.
• Development of complex scientific workflows on high-performance computing platforms.
• Demonstrated ability to work independently.

Benefits

• PPO or High Deductible medical insurance with a 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).

Work location is onsite in Los Alamos, NM. The appointment is for two years, with the possibility of a third-year extension based on performance evaluation. Candidates may be considered for fellowships supported by the Center for Nonlinear Studies (CNLS) or G. T. Seaborg Institute. Outstanding candidates may be considered for a Director's Postdoc Fellowship or Distinguished Postdoc Fellowships.

Due to federal restrictions, citizens of the People's Republic of China (including Hong Kong and Macau), 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 supporting national security laboratories and nuclear weapons production, including Los Alamos National Laboratory.

This position requires a Q clearance, which typically requires U.S. citizenship. Selected applicants will undergo a background investigation. Additional authorization may be required for access to classified information.