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Position: Postdoctoral Appointee - Bayesian Inference & Machine Learning
Institution: Sandia National Laboratories
Location: Livermore, California, United States
Duties: Work as part of a technical team conducting innovative research foundational or applied data science; Apply data science capabilities to scientific and engineering applications relevant to Sandia’s diverse mission space; Publish outstanding new developments in peer-reviewed scientific journals; Contribute to development of open-source software; Interact and cooperate with a diverse set of colleagues
Requirements: Ph.D. in computer science, applied mathematics, or a related engineering or science subject area; Technical expertise in at least one of Bayesian inference, optimal experimental design, variational inference, or Monte Carlo methods; Experience with neural networks and using ML platforms like TensorFlow or PyTorch; Publication record indicative of relevant research expertise; Excellent written and verbal communication and interpersonal skills; Demonstrated expertise in one or more of the following areas: applied mathematics, statistics, uncertainty quantification, reduced order modeling, feature identification techniques, machine learning, compression, linear/multilinear methods, nonlinear optimization, Bayesian inference
   
Text: Job Description Home Actions Close Previous Job Postdoctoral Appointee - Bayesian Inference & Machine Learning Next Job Apply for Job Job ID 673271 Location Livermore, CA Full/Part Time Full-Time Regular/Temporary Temporary Add to Favorite Jobs Email this Job What Your Job Will Be Like We are seeking a Postdoctoral Appointee in the areas of Bayesian inference and machine learning. On any given day, you may be called on to: Work as part of a technical team conducting innovative research foundational or applied data science Apply data science capabilities to scientific and engineering applications relevant to Sandia’s diverse mission space Publish outstanding new developments in peer-reviewed scientific journals Contribute to development of open-source software Interact and cooperate with a diverse set of colleagues Qualifications We Require Ph.D. in computer science, applied mathematics, or a related engineering or science subject area Technical expertise in at least one of Bayesian inference, optimal experimental design, variational inference, or Monte Carlo methods. Experience with neural networks and using ML platforms like TensorFlow or PyTorch. Publication record indicative of relevant research expertise Excellent written and verbal communication and interpersonal skills Demonstrated expertise in one or more of the following areas: applied mathematics, statistics, uncertainty quantification, reduced order modeling, feature identification techniques, machine learning, compression, linear/multilinear methods, nonlinear optimization, Bayesian inference. Qualifications We Desire Ability to work in collaborative, interdisciplinary research environments on problems comprising diverse application domains A background in solving practical problems in science and engineering that involve encounters with real-world data Experience in high performance, distributed, or parallel computing Expertise in Bayesian Neural Networks Proven research community leadership through activities such as participation in student or professional organizations, outreach activities, etc. Related professional experience such as internships in industry or at other national labs, participation in visiting research programs, etc. Proven software development experience in C++, C, Matlab, R, Python, Julia, or related languages 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. About Our Team Sandia's Extreme-scale Data Science & Analytics department provides strong expertise in data science and analytics, supporting Sandia's physical sciences and engineering mission partners by enabling the extraction of critical insights from extreme-scale observations, simulations and experiments. Capabilities in the department combine recent advances in applied mathematics and computer science, with expertise in uncertainty quantification, reduced order modeling, linear/multilinear methods, nonlinear optimization, and feature identification techniques. These capabilities complement and leverage existing research in other Sandia departments. 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 schedules, 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. 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. 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.
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