Postdoctoral Appointee: Machine Learning for Materials in Hydrogen and Energy Sustainability
- Sandia National Laboratories
- Location: Livermore, CA
- Job Number: 7091276 (Ref #: 679979)
- Posting Date: Dec 9, 2021
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. : Sandia demonstrates its commitment to public safety in the national interest by requiring that all new hires be fully vaccinated or have an approved medical or religious accommodation before commencing employment. The requirement also applies to those who are telecommuting and working virtually. Any concerns about the ability to meet this requirement should be directed to HR Solutions at (505) 284-4700. : We are seeking a Postdoctoral Appointee/Scientist to apply machine learning, first principles, and classical simulations to materials discovery. Application areas may include materials for production, storage, and generation for hydrogen and other energy needs. A central theme in this effort is the rational design of new materials by changing the chemical composition, the arrangement of the atoms or molecules in crystalline or amorphous configurations, and the size, shape, and orientation of nanoparticles, films, crystals or other nano- or macroscopic units. On any given day, you may be called on to: + Demonstrate the creativity and know-how to + Develop new materials’ featurization strategies and + Adapt pioneering machine and deep learning techniques to directly predict materials properties that are not achievable within currently established methodologies. + Use first principles/ab initio computational 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 and kinetic properties of materials + Use interpretable machine learning techniques to discover new structure-function relationships from experimental or computational data that will in-turn be used to design novel materials with properties and behavior that are enhanced over existing options + Conduct research that would involve extensive collaboration with team members engaged in modeling, material synthesis, and use of advanced characterization tools + Play a major role in discovery of new materials, as well as advancing the science of these through the use of innovative computational and data science tools
+ PhD in a relevant STEM field (e.g. chemistry, materials science, physics) or a PhD anticipated in calendar year 2022 + Research emphasis of applying theory, computational methods, or machine learning to address materials science problems + Experience with one or more of the following computational techniques: machine learning, density functional theory, ab initio quantum chemistry, atomistic modeling and simulation + Strong written and oral communication skills, as shown by a record of first-author or coauthored scientific publications in peer-reviewed journals and presentations at scientific conferences
+ Hands-on experience modeling material systems (through, e.g. density functional theory, monte carlo, molecular dynamics, or other modeling tools) + Experience with open-source materials science libraries and databases like ase, pymatgen, Materials Project, OQMD, etc. + Experience with interpretable machine learning, active learning or genetic algorithms
The Energy Nanomaterials Department is a research organization specializing in the discovery, synthesis and characterization of nanomaterials for energy and national security applications. Projects and capabilities in the department's research portfolio include nanoporous materials for sensing, catalysis, energy harvesting, and gas storage; materials for energy and hydrogen storage; macine learning; and nanoscale materials characterization including extensive electron microscopy, atomic force microscopy, nanoindentation instrumentation, and X-ray diffraction.
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 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: 679979
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.