Lawrence Berkeley National Lab’s (LBNL) Applied Mathematics and Computational Research Division has an opening for a Postdoctoral Scholar (Computational Research) in applying machine learning tools to optimization, sampling and uncertainty quantification. The Center for Computational Sciences and Engineering (CCSE) develops and applies advanced computational methodologies to solve large-scale scientific and engineering problems arising in DOE mission areas involving energy, environment, science and industrial technology. You will actively contribute to CCSE’s optimization, sampling and uncertainty quantification efforts. 

In this exciting role, you will be doing research with a combination of elements of applied mathematics, statistics, machine learning and computational science. The successful applicant will develop, test, and benchmark new algorithms for high-dimensional inference and data analysis, using machine learning models to accelerate computations. The postdoc will tightly collaborate with Prof. Seljak’s group at UC Berkeley. For more information about the optimization effort in the Center for Computational Sciences and Engineering, please visit https://optimization.lbl.gov/. More information about research in Prof. Seljak’s group can be found at http://bccp.berkeley.edu.

What You Will Do:

• Perform basic research in numerical optimization and/or sampling based uncertainty quantification.
• Develop, test, benchmark, and tune algorithms.
• Work in a multidisciplinary team environment including mathematicians, computer and computational scientists, and domain scientists.
• Author peer-reviewed journal articles and contribute to research proposals

What is Required:

• Ph.D. in Applied Mathematics, Statistics, Machine Learning, Computational Science or related areas within the last 3 years, with a strong research background in numerical optimization and/or sampling.
• Demonstrated research experience in the development and/or applications of computational optimization and/or sampling algorithms.
• Demonstrated knowledge of and experience in using machine learning methods. 
• Excellent Python programming skills.
• Ability and desire to work as part of a cross-disciplinary team.
• Excellent oral and written communication skills.

Desired Qualifications:

• Strong interpersonal communication skills are a plus.

Notes:

• This is a full time, M-F, exempt from overtime pay (monthly paid), 2 year postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs. 
• Salary for postdoctoral positions depends on years of experience post-degree and will be predetermined based on postdoctoral step rates.
• This position is represented by a union for collective bargaining purposes.
• 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 will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.

Equal Employment Opportunity:

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 that vaccination requirements have been met or submitting a request for Exception or Deferral. Visit covid.lbl.gov for more information.

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