Sparse Linear Algebra and Machine Learning Postdoc Fellow

Sparse Linear Algebra and Machine Learning Postdoc Fellow - 103159
Division: AM-Applied Mathematics and Computational Research

The Scalable Solvers Group in the Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (LBNL) is seeking a Postdoctoral Fellow to develop parallel sparse linear solvers and preconditioners, integrate them into DOE science codes, and develop scalable scientific machine learning (SciML) and uncertainty quantification (UQ) methods for large-scale modeling and simulations.

What You Will Do:
• Develop next-generation sparse factorization based algebraic solvers and preconditioners for multi-node GPU platforms.
• Develop novel algorithms in the Bayesian statistical Gaussian Process framework, leading to robust and scalable UQ methods.
• Optimize the newly developed codes on emerging supercomputers built from multi- and many core processors with GPU accelerators.
• Participate in a multidisciplinary team involving mathematicians, computer scientists, and domain scientists for developing and deploying advanced solver and SciML techniques in the solution of scientific and engineering problems.
• Document work and results in the form of journal papers and conference proceedings.
• Present work and results at scientific meetings.

What is Required:
• Ph.D. degree in Computer Science, Applied Math, Statistics, or Computational Science.
• Knowledge of numerical linear algebra, sparse matrices, and randomized algorithms.
• Knowledge of statistical and machine learning algorithms.
• Coding experience in parallel sparse matrix computations.
• Experience of parallel programming in high-level languages, such as C/C++, MPI, and OpenMP.
• Proven consensus builder in a highly collaborative environment.
• Excellent written and oral communication skills.

Desired Qualifications:
• Experience in parallel programming for GPUs.

Notes:
• This is a full-time, 2 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 3 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.
• The monthly salary range for this position is $8,321-$9,646 and is expected to start at $8,321 or above. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral experience.
• 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.
• This position is eligible for a hybrid work schedule - a combination of weekly teleworking and performing work on site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work schedules are dependent on business needs. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab.

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

How To Apply
Apply directly online at http://50.73.55.13/counter.php?id=293412 and follow the on-line instructions to complete the application process.

Berkeley Lab is committed to inclusion, diversity, equity and accessibility 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:
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