Postdoctoral Appointee - Computational Materials Chemistry, Materials Informatics, and Predictive Modeling, Onsite

Postdoctoral Appointee - Computational Materials Chemistry, Materials Informatics, and Predictive Modeling, Onsite

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://www.sandia.gov

* These benefits vary by job classification.

What Your Job Will Be Like:

We are seeking a POSTDOCTORAL APPOINTEE with expertise in computational materials chemistry, materials informatics, and predictive modeling to join an interdisciplinary team working on a broad range of materials challenges. Current application areas include understanding materials behavior under extreme conditions, developing structure property relationships for polymeric materials, and accelerating discovery for critical materials recovery. In this role, you will work closely with synthetic materials scientists and polymer chemists across multiple Sandia sites to develop data-driven and computational approaches that connect experimental observations with predictive understanding.

This is an exceptional opportunity for a scientist interested in bridging scientific gaps through the development of computational and machine learning based tools grounded in experimental data, and in applying those tools across multiple materials systems and mission-relevant applications.

As a key contributor, you will:

Develop and apply computational, statistical, and machine-learning approaches to analyze heterogeneous experimental and simulation datasets in order to establish structure property performance relationships in materials.

Build predictive and interpretable models that connect material chemistry, morphology, processing history, and environmental exposure to measurable performance outcomes.

Work closely with experimental materials scientists and chemists to integrate computational modeling with characterization data and guide the interpretation of experimental results.

Contribute to the design of next-step experiments or screening strategies using uncertainty-aware modeling, active learning, or other data-driven approaches.

Collaborate across multidisciplinary teams that may include researchers from Sandia, other national laboratories, universities, and industry.

Communicate technical progress, milestones, and research findings through written reports, peer-reviewed publications, and presentations at internal and external meetings.

Qualifications We Require:

PhD in materials science and engineering, chemistry, chemical engineering, physics, applied mathematics, computer science, data science, or a related field.

Demonstrated experience building, evaluating and applying machine learning based models to experimental and/or computational datasets.

Familiarity with machine learning techniques for developing structure- property relationships in distinct materials systems.

Experience with probabilistic surrogate models or active learning approaches for scientific discovery.

Ability to obtain and maintain DOE Q clearance

Strong record of technical accomplishments and written communication as evidenced by peer-reviewed publications.

Due to the nature of the work, the selected applicant must be able to work onsite.

Qualifications We Desire:

Strong verbal and written communication skills and ability to work effectively in a highly collaborative, multidisciplinary team environment.

Background in computational materials science, computational chemistry, materials informatics, or scientific machine learning.

Experience developing machine-learning models for structure
¿ property or structure
¿ function relationships.

Experience with uncertainty quantification,…