Job DescriptionThis posting will be open for application submissions for a minimum of seven (7) calendar days, including the ‘posting date’. Sandia reserves the right to extend the posting date at any time. : We seek a postdoctoral appointee to apply state-of-the-art scientific machine learning tools to develop data-driven approaches to efficient control and diagnostics of additive manufacturing and electrochemical processes of thin films. The successful candidate will work with a diverse team of modelers, experimentalists and applied mathematicians to develop a machine learning framework for material science problems. We are committed to nurturing a culture compatible with a broad group of people and perspectives in accordance with the changing makeup of the workforce. In support of this vision, the center actively recruits applicants from diverse groups of backgrounds and fosters an inclusive community.In this role, you will work collaboratively on a multidisciplinary research team conducting fundamental algorithmic research. On any given day, you may be called on to: + Conduct leading-edge research in Scientific Machine Learning (SciML), including both physics-informed techniques incorporating engineering/physics models and traditional image analysis of high throughput material science experiments + Work towards publishing new developments in high-profile peer-reviewed scientific journals or refereed conference proceedings; contribute to development of open-source software for high performance computing environments + Interact with a diverse set of colleagues from both your own field, applications specialists, and others + Travel as needed to support projects + Work with export-controlled information which requires US Person status Join us and work towards your goals while making a difference!
+ Possess, or are pursuing, a PhD in mathematics, material science, physics, computer science, or a related engineering or natural science field (conferred within 3 years prior to employment) + Familiarity with optimization or deep learning, as evidenced by either completion of a graduate class that covered optimization or deep learning or use of optimization or deep learning in a research setting. + Training in continuum modeling using differential equations, with particular preference for those with training in numerical solution of differential equations for surface physics. Due to U.S. export-control laws, only U.S. Persons (U.S. citizens, lawful permanent residents, asylees, or refugees) are eligible for consideration.
+ Knowledge or experience of additive manufacturing processes for thin films, including: physical vapor deposition, electroplating, or laser powderbed fusion + Research experience in numerical optimization for engineering design, particularly with a focus on Bayesian methods and uncertainty quantification + Expertise in solid mechanics, fluid mechanics, or electrochemistry + methods, domain decomposition, matrix sketching, or hiearchical matrices + Experience with Tensorflow/pyTorch, and the application of machine learning (ML) techniques to large datasets + Passion for applying machine learning and computational methods to problems in science and engineering + Strong written and oral interpersonal skills + A dedication to encouraging an inclusive culture, as proven in your application materials + Proven track record teaming in an interdisciplinary R&D environment + Significant experience in code development, potentially including software design using object-oriented programming, high-performance computing, distributed or parallel computing, and/or coding for computer architectures + A background in solving problems in science and engineering that involve encounters with real world data + Proven research community leadership through activities such as participation in student or professional organizations, service on committees, workshop and/or conference organization, and editorial roles + Ability to acquire and maintain a DOE security clearance
The Computational Mathematics Department (01442) conducts research in computational and applied mathematics motivated by science and engineering applications of interest to Sandia National Laboratories and the U.S. Department of Energy. We interact and collaborate with a broad range of Sandia and DOE staff and also maintain a research presence in the external professional community by collaborating with universities and industry, publishing peer-reviewed literature, participating in professional societies, and refereeing and editing for journals.
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 vacations, 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.
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. 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.
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. : 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 three 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.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.