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Postdoctoral Scholar - Applied Computational Sciences

Job Description


The Energy Technology Area at Berkeley Lab is seeking a Postdoctoral Scholar from Applied Mathematics, Statistics, Computational Sciences, and/or Data Science to work on designing Artificial Intelligence and Machine Learning techniques (AI/ML), with the end goal of improving autonomous battery design optimization and battery life-cycle testing. All algorithmic developments will be applied to data coming from the Energy Storage and Distributed Resources Division of the Lawrence Berkeley Lab. 

 

In this exciting role, you will combine mathematics, statistics, and computing with instrument and domain science to expand efforts of designing AI/ML methods for intelligent and autonomous battery designs. 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. The candidate will be working closely with scientists, engineers, and software developers to turn new mathematics and statistics into Python software packages.

  

What You Will Do:

You will join in the development of next-generation statistical tools (for instance Gaussian Processes) algorithms and code for stochastic function approximation and autonomous experimentation with the end goal of optimal battery design. Part of this effort is understanding and improving early-failure predictions for batteries. You will report to the project lead Stephen Harris. Specific activities will include:

  • Advance algorithms for early-failure prediction and testing. This includes uncertainty quantification and machine learning algorithms.
  • Design and advance algorithms for parameter-space exploration and design optimization (stochastic processes Bayesian optimization, kernel methods).
  • Design usable Python software.
  • Write papers and give talks at conferences and workshops.
  • 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:

  • 3+ years of relevant experience beyond a degree in applied mathematics, statistics, or computer science.
  • Experience and a strong interest in scientific software development or research software engineering.
  • Experience using and developing in Python.
  • Experience in Python coding.
  • Demonstrated record of scientific excellence through publications, talks, 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.

 

Additional Desired Qualifications:

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

 

For Consideration, please apply by Dec 9, 2022 with the following application materials:

  • Cover Letter - Describe your interest in this position and the relevance of your background.
  • Curriculum Vitae (CV) or Resume.

 

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.
  • This position is eligible for a hybrid work schedule - a combination of 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.

 

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.
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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