Postdoctoral Appointee, GenAI - Hybrid; CA​/NM

Postdoctoral Appointee, GenAI - Hybrid (CA/NM)

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting‑edge work in a broad array of areas. Some of the main reasons we love our jobs:

Challenging work with amazing impact that contributes to security, peace, and freedom worldwide

Extraordinary co-workers

Some of the best tools, equipment, and research facilities in the world

Career advancement and enrichment opportunities

Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten‑hour days each week) compressed workweeks, part‑time work, and telecommuting (a mix of onsite work and working from home)

Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

World-changing technologies. Life-changing careers. Learn more about Sandia at: http://(Use the "Apply for this Job" box below)..gov

* These benefits vary by job classification.

Are you looking to do creative and innovative research in cheminformatics, data, and predictive science? Do you want to increase your engagement with research that supports the development of material science and engineering? Sandia's Materials Lifecycle Management department is seeking a highly dedicated and innovative Postdoctoral Appointee to join the Material, Physical, and Chemical Sciences Center.

We are seeking a Postdoctoral Appointee/Scientist to apply electronic structure calculations, machine learning, and/or first principles simulations to accelerate materials discovery. Research areas may broadly span organic and inorganic materials modeling for hydrogen sinks, storage, or utilization), carbon capture, or other applications. A central theme in this effort is the rational design of new materials using existing, improved, and novel GenAI algorithms for generating new molecules or crystal structures and compositions or other nano‑ or macroscopic units.

Excellent communication skills are required to convey results with multi‑disciplinary scientific teams and help prioritize and direct experimental validation efforts.

On any given day, you may be called on to:

Demonstrate the creativity and know‑how to utilize and develop GenAI models for finding novel materials of interests, develop new materials
¿ featurization strategies, and adapt state‑of‑the‑art machine and deep learning techniques to directly predict materials properties that are not achievable within currently established methodologies.

Use Density Functional Theory (or other first principles techniques) to acquire any data necessary to train such models when it does not already exist.

Integrate the outputs of machine learning models with other physics‑based models or classical simulations to predict the thermodynamic, kinetic, or electronic properties of materials.

Mentor technologists and interns

Work collaboratively to execute, track, and refine project plans with an integrated multi‑disciplinary team that may include members from national labs, universities, and industry

Communicate your research through writing scientific papers, publishing in peer‑reviewed journals, and presenting findings at seminars and conferences

Work to execute, monitor, and refine project plans and proposals

This position is eligible for telecommuting and the selected applicant must live within a reasonable distance for commuting to the assigned work location when necessary.

PhD acquired within the last 5 years in or related to material science, engineering, applied mathematics, physics, chemistry, bioinformatics, computer science, machine learning, artificial intelligence, or related field with an emphasis on building, evaluating, and applying predictive models.

Active learning, GenAI techniques to iteratively discover better materials

Familiarity with interpretable machine learning techniques to discover new structure‑function relationships from experimental or computational data

Record of technical…