Postdoc in AI/ML-Driven Materials & Chemistry

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 65,000 - 90,000; Full time

Benefits offered by this job

Medical insurance

Dental and vision insurance

Life and disability insurance

Parental leave

401(k) plan

Tuition assistance

Flexible schedules

Onsite gyms

Relocation assistance

Job summary

Los Alamos National Laboratory (LANL) invites postdoctoral researchers to join the XCP-AI4ND and T-1 groups to advance integrated molecular simulation, design, and optimal experiment planning for f-element materials.

You will work at the intersection of quantum chemistry, machine learning, and agentic-AI on problems in lanthanide/actinide chemistry, with cross-disciplinary collaboration across theory and experiment.

Qualifications

• Ph.D. completed within the last five years.
• Experience with AI/ML scientific workflows in computational chemistry or materials science.
• Experience applying ML/data science/uncertainty quantification to molecular or condensed-phase systems.

Responsibilities

• Develop integrated molecular simulation, molecular design, and optimal experiment design for f-element materials.
• Collaborate across quantum chemistry, machine learning, and agentic-AI with theorists, experimentalists, and data scientists.

Skills

Machine learning

Data science

Uncertainty quantification

Surrogate modeling

AI/ML workflows

Electronic structure (DFT)

Atomistic simulations

Independent work

Communication skills

Education

Ph.D. in Chemistry, Physics, Materials Science, Chemical Engineering, Applied Mathematics, or related field

Job description

Los Alamos National Laboratory (LANL) invites postdoctoral researchers to join the XCP-AI4ND and T-1 groups to advance integrated molecular simulation, design, and optimal experiment planning for f-element materials.

You will work at the intersection of quantum chemistry, machine learning, and agentic-AI on problems in lanthanide/actinide chemistry, with cross-disciplinary collaboration across theory and experiment.