Job description

About Sandia: 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: sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: We are seeking a highly motivated and driven Postdoctoral Appointee to join a multidisciplinary team conducting research at the intersection of computational science, applied mathematics, and data-driven modeling. The selected candidate will have the opportunity to contribute to two complementary research efforts: (i) developing AI/ML methods for uncertainty quantification in multiscale materials modeling and (ii) developing domain decomposition-based hybrid modeling approaches that couple full-order, reduced-order, and data-driven models. For the first research effort, the selected candidate will contribute to a multiscale effort to predict metallic microstructure evolution and mechanical failures in extreme environments. Ideal candidates will be creative problem solvers with a solid foundation in uncertainty quantification, AI/ML, and materials modeling, along with experience in scientific computing, as demonstrated by relevant publications and code contributions. On any given day, you may be called on to conduct research to develop effective supervised and unsupervised learning algorithms to facilitate large-scale computational studies of phase field and polycrystalline structure evolution. For the second research effort, the selected candidate will conduct research on domain decomposition-based approaches for hybrid modeling and simulation. This work will focus on developing methods for coupling models of different fidelities and/or mathematical representations including full-order models (FOMs) and data-driven reduced-order models (ROMs) within a common domain decomposition framework. A particular area of interest is the development of adaptive approaches that enable online switching between ROMs and FOMs within individual subdomains as solution features evolve during a simulation, with the goal of balancing computational efficiency and predictive accuracy. On a typical day, you may be called on to develop and implement algorithms related to the creation of accurate and efficient adaptive hybrid models, applied primarily to solid mechanics exemplars. The selected candidate will work closely with Sandia Principal Investigators and will also collaborate with scientists from other national laboratories and universities involved in these projects. Due to the nature of the work, the selected candidate must be able to work onsite. Qualifications We Require: • PhD in a field of physical sciences, applied mathematics, engineering, or other relevant field conferred within five years prior to employment. • Knowledge and expertise in machine learning and/or uncertainty quantification. • Knowledge and expertise in projection-based reduced order modeling and/or operator inference. • Knowledge and expertise in computational science and/or software development. • This position requires access to export-controlled or ITAR information. Only U.S. persons (citizens, lawful permanent residents, asylees or refugees) are eligible for consideration. Qualifications We Desire: • Proficiency in using open-source machine learning libraries such as PyTorch and/or JAX for developing, training, and deploying advanced machine learning models tailored to complex scientific problems. • Expertise in operator learning and generative models with a focus on their applications in the physical sciences. • Experience with reduced order modeling and latent space representations. • Experience with computational solid mechanics. • Experience programming in Julia. • Strong programming skills in C++ and Python and integration of machine learning solutions into existing scientific workflows. • Collaborative research experience. • Excellent interpersonal and communication skills. About Our Team: The Quantitative Modeling and Analysis Department conducts research, development, and systems engineering in computer science and engineering to address important, complex national security problems. Key research areas include uncertainty quantification, optimization, inference modeling, data analysis, and algorithm development. Much of our application development work focuses on verification, validation and uncertainty quantification of complex problems. We provide trusted design and software development for large, operations software as well as detailed scientific computing software. The work is focused on providing efficient, validated information and technologies for use in predictive modeling and decision making. 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: This position does not currently require a Department of Energy (DOE) security clearance. Sandia will conduct a pre-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Furthermore, employees in New Mexico need to pass a U.S. Air Force background screen for access to Kirtland Air Force Base. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause access to be denied or terminated, resulting in the inability to perform the duties assigned and subsequent termination of employment. Under federal law, citizens and agents of the People's Republic of China, the Islamic Republic of Iran, the Democratic People's Republic of North Korea, and the Russian Federation are generally prohibited from accessing Sandia National Laboratories. Accordingly, such individuals will not be considered for employment unless they are also a citizen of the United States. If hired without a clearance and it subsequently becomes necessary to obtain and maintain one for the position, or you bid on positions that require a clearance, a pre-processing background review may be conducted prior to a required federal background investigation. Applicants for a DOE security clearance need to be U.S. citizens. 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. Members of the workforce (MOWs) hired at Sandia who require uncleared access for greater than 179 days during their employment, are required to go through the Uncleared Personal Identity Verification (UPIV) process. Access includes physical and/or cyber (logical) access, as well as remote access to any NNSA information technology (IT) systems. UPIV requirements are not applicable to individuals who require a DOE personnel security clearance for the performance of their SNL employment or to foreign nationals. The UPIV process will include the completion of a USAccess Enrollment, SF-85 (Questionnaire for Non-Sensitive Positions) and OF-306 (Declaration of for Federal Employment). An unfavorable UPIV determination will result in immediate retrieval of the SNL issued badge, removal of cyber (logical) access and/or removal from SNL subcontract. All MOWs may appeal the unfavorable UPIV determination to DOE/NNSA immediately. If the appeal is unsuccessful, the MOW may try to go through the UPIV process one year after the decision date. EEO: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or 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. Job ID: 698990 Job Family: 92 Regular/Temporary Position: T Full/Part-Time Status: F

Requirements

Must-haves

The selected candidate will work closely with Sandia Principal Investigators and will also collaborate with scientists from other national laboratories and universities involved in these projects.
Due to the nature of the work, the selected candidate must be able to work onsite.
PhD in a field of physical sciences, applied mathematics, engineering, or other relevant field conferred within five years prior to employment.
Knowledge and expertise in machine learning and/or uncertainty quantification.
Knowledge and expertise in projection-based reduced order modeling and/or operator inference.
Knowledge and expertise in computational science and/or software development.
This position requires access to export-controlled or ITAR information. Only U.S. persons (citizens, lawful permanent residents, asylees or refugees) are eligible for consideration.
Proficiency in using open-source machine learning libraries such as PyTorch and/or JAX for developing, training, and deploying advanced machine learning models tailored to complex scientific problems.
Expertise in operator learning and generative models with a focus on their applications in the physical sciences.
Experience with reduced order modeling and latent space representations.
Experience with computational solid mechanics.
Experience programming in Julia.
Strong programming skills in C++ and Python and integration of machine learning solutions into existing scientific workflows.
Collaborative research experience.
Excellent interpersonal and communication skills.
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.
This position does not currently require a Department of Energy (DOE) security clearance.
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.
Job ID: 698990
Job Family: 92
Regular/Temporary Position: T
Full/Part-Time Status: F

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpythongo

Platform: careers-html
Effort: medium

Benefits

Benefits detected in the listing.

401k