Postdoctoral Scholar (Computational Research)

📁
Postdoctoral Fellow
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CR-Computational Research
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93915 Requisition #

Lawrence Berkeley National Lab’s (LBNL) 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 of Full Vaccination or submitting a request for Exception or Deferral. Visit covid.lbl.gov 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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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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The Lawrence Berkeley National Laboratory provides accommodation to otherwise qualified internal and external applicants who are disabled or become disabled and need assistance with the application process. Internal and external applicants that need such assistance may contact the Lawrence Berkeley National Laboratory to request accommodation by telephone at 510-486-7635, by email to eeoaa@lbl.gov or by U.S. mail at EEO/AA Office, One Cyclotron Road, MS90R-2121, Berkeley, CA 94720. These methods of contact have been put in place ONLY to be used by those internal and external applicants requesting accommodation.