Lawrence Berkeley National Laboratory

Position ID: 2545-CSAMPS [#20422, 60778]
Position Title: Computational Science/Applied Mathematics Postdoctoral Scholar
Position Type: Postdoctoral
Position Location: Berkeley, California 94720, United States
Subject Area: Computational Science
Application Deadline: 2022/09/19 11:59PMhelp popup finished (2022/08/18, finished 2023/03/25, listed until 2023/02/18)
Position Description:    

*** this position has been closed. ***

Computational Science/Applied Mathematics Postdoctoral Scholar - 96602 Division: AM-Applied Mathematics and Computational Research

Lawrence Berkeley National Lab’s (LBNL, Applied Mathematics and Computational Research Division ( has an opening for a Computational Science/Applied Mathematics Postdoctoral Scholar with a strong background in applied mathematics and computational science to work on developing new methodologies and algorithms to advance Gaussian-Process-driven uncertainty quantification, with the end goal of improving procedures for autonomous data acquisition. All algorithmic developments will be applied to data coming from one or more instruments and other data sources within the network of DOE’s experimental facilities.

In this exciting role, you will combine mathematics, statistics, and high-performance computing to create new uncertainty-quantification-driven machine learning algorithms to support autonomous-experimentation efforts across the DOE experimental facilities, for instance, but not limited to, the Advanced Light Source, the National Synchrotron Light Source II, and the Molecular Foundry. Autonomous experimentation is used there to confidently and efficiently navigate parameter spaces without human interference. You will work as part of a collaborative team to integrate new methods for uncertainty quantification from data and state-of-the-art instrument infrastructure. You will be working closely with scientists, engineers, and software developers to turn new mathematics into high-performance-computing-ready software packages.

What You Will Do: • Explore hard constraints on a Reproducing Kernel Hilbert Space via stationary kernel designs. • Develop general approaches for flexible non-stationary kernel designs. Turn the advancements into a framework for multi-task learning. • Help to extend discovered kernel designs for use in non-linear spaces. • Publish papers and present at conferences/workshops. • Spearhead the HPC-ready implementation of the algorithms. • Work closely with LBNL scientists and collaborators to deploy and evaluate the software. • Provide training to colleagues and write excellent documentation, to elevate the work from a proof of concept to maintainable, long-lasting infrastructure.

What is Required: • PhD in applied mathematics, computer science, statistics, or a related field. • Experience and a strong interest in scientific software development or research software engineering. • Experience using and developing in Python. • Experience with high-performance computing (HPC) ideally in Python. • Demonstrated record of scientific excellence through publications, talks, or software deliverables. • Ability to work collaboratively with a diverse team of scientists and engineers. • Experience contributing to a scientific software project in a team environment, which might include co-developing an internal project or contributing to community-based open-source software.

Desired Qualifications: • Experience with uncertainty quantification, ideally Gaussian or general stochastic processes. • A working understanding of kernel methods and Reproducing Kernel Hilbert Spaces. • Background in differential geometry. • Demonstrated record in collaborative software development, especially in distributed teams. • Experience with the DASK library. • Interest in experiencing large-scale experiments.

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Notes: • This is a full-time 1 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 4 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree. • This position is represented by a union for collective bargaining purposes. • Salary will be predetermined based on postdoctoral step rates. • This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment. • Work may be performed on-site, hybrid, full-time telework or remote modes. Work must be performed within the United States.

How To Apply Apply directly online at and follow the on-line instructions to complete the application process.

Based on University of California Policy - SARS-CoV-2 (COVID-19) Vaccination Program and U.S Federal Government requirements, Berkeley Lab requires that all members of our community obtain the COVID-19 vaccine as soon as they are eligible. As a condition of employment at Berkeley Lab, all Covered Individuals must Participate in the COVID-19 Vaccination Program by providing proof of Full Vaccination or submitting a request for Exception or Deferral. Visit ( for more information.

Berkeley Lab is committed to Inclusion, Diversity, Equity and Accountability (IDEA, and strives to continue building community with these shared values and commitments. Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab's mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

Equal Opportunity and IDEA Information Links: Know your rights, click here ( for the supplement: Equal Employment Opportunity is the Law and the Pay Transparency Nondiscrimination Provision ( under 41 CFR 60-1.4.

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Contact: Nicolette Carrroll
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Berkeley, CA 94720
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