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

Sandia National Laboratories; California (MO); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

Flexible work arrangements

Relocation assistance

Generous vacation

401k benefits

Job summary

Sandia National Laboratories invites applications for a Postdoctoral Appointee in Computational Materials Chemistry, Materials Informatics, and Predictive Modeling. The role centers on developing data-driven and computational tools to understand materials behavior under extreme conditions and to accelerate discovery across multiple materials systems.

You will build and apply machine-learning models, work with experimental teams, and contribute to design of next-step experiments using

Qualifications

• PhD in a relevant STEM field with strong research background.
• Experience building, evaluating and applying ML models to experimental/computational datasets.
• Familiarity with ML for structure–property relationships in materials.

Responsibilities

• Develop and apply computational, statistical, and machine-learning approaches to analyze heterogeneous datasets to relate structure, properties and performance in materials.
• Build predictive and interpretable models linking chemistry, morphology, processing history and environment to outcomes.
• Collaborate with experimental scientists to integrate modeling with characterization data and guide interpretation of results.
• Design next-step experiments or screening strategies using uncertainty-aware modeling and active learning.

Skills

Machine learning

Data analysis

Python

Statistics

Uncertainty quantification

Active learning

Education

PhD in materials science and engineering, chemistry, chemical engineering, physics, applied mathematics, computer science, data science

Job description

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, out-of-domain detection, surrogate modeling, or active-learning / Bayesian experimental design methods.

Experience working with large, heterogeneous, or multi-modal datasets, including imaging, spectroscopy, or materials property data.

Familiarity with polymers, soft materials, aging/degradation, hydrogen compatibility, or related materials-performance problems.

Strong programming and data-management skills, including experience with Python-based data science tools and, where relevant, databases or structured data libraries.

Interest in bridging computational methods with experimental materials science and characterization.

About Our Team:

The Materials Chemistry Department is a group of Chemists, Material Scientists, and Chemical Engineers who actively supports the nation's nuclear weapons stockpile by providing materials expertise to SNL's non-nuclear components. We use our understanding of chemical properties to design, evaluate and exploit monomers, polymers and functional materials. Our core competencies span foams, composites, adhesives, plastics, surface finishing and electrochemical deposition. We have the facilities for materials characterization, design prototyping and WR processing.

Posting Duration:

This posting will be open for application submissions for a minimum of three (3) calendar days, including the 'posting date'. Sandia reserves the right to extend the posting date at any time.

Security Clearance:

Sandia is required by DOE to conduct a pre-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Applicants for employment need to be able to obtain and maintain a DOE Q-level security clearance, which requires U.S. citizenship. If you hold more than one citizenship (i.e., of the U.S. and another country), your ability to obtain a security clearance may be impacted.

Applicants offered employment with Sandia are subject to a federal background investigation to meet the requirements for access to classified information or matter if the duties of the position require a DOE security clearance. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause a clearance to be denied or terminated by DOE, resulting in the inability to perform the duties assigned and subsequent termination of employment.

EEO:

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status and any other protected class under state or federal law.

NNSA Requirements for MedPEDs:

If you have a Medical Portable Electronic Device (MedPED), such as a pacemaker, defibrillator, drug-releasing pump, hearing aids, or diagnostic equipment and other equipment for measuring, monitoring, and recording body functions such as heartbeat and brain waves, if employed by Sandia National Laboratories you may be required to comply with NNSA security requirements for MedPEDs.

If you have a MedPED and you are selected for an on-site interview at Sandia National Laboratories, there may be additional steps necessary to ensure compliance with NNSA security requirements prior to the interview date.

Position Information:

This postdoctoral position is a temporary position for up to one year, which may be renewed at Sandia's discretion up to five additional years. The PhD must have been conferred within five years prior to employment.

Individuals in postdoctoral positions may bid on regular Sandia positions as internal candidates, and in some cases may be converted to regular career positions during their term if warranted by ongoing operational needs, continuing availability of funds, and satisfactory job performance.