Refine Search Clear All


Career Areas

Show More

Experience Level


Employment Type


Location


Organization

Show More

Security Clearance

Keyword Search

Back to open positions

Reinforcement Learning Postdoctoral Researcher

Entry Level | Full-time
Engineering | livermore, CA | 05/07/2021

Apply Now  

Company Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is essential to innovation and creativity for continued success of the Laboratory’s mission.

Job Description

We have multiple openings for a Postdoctoral Researcher to conduct basic and applied research in Reinforcement Learning for optimal sequential decision making under uncertainty, on real world problems in healthcare, cyber security, and national security applications. These positions are in the Computational Engineering Division (CED) within the Engineering Directorate.

In this role you will 

  • Conduct research in reinforcement learning to enable development of new state-of-the-art algorithms for Laboratory problem domains.
  • Design, implement, and analyze techniques in reinforcement learning.
  • Explore techniques for controlling simulations using approaches such as deep reinforcement learning, bandit optimization, evolutionary algorithms, model based methods, and stochastic control.
  • Contribute to and actively participate in the conception, design, and execution of research to address defined problems.
  • Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
  • Collaborate with others in a multidisciplinary team environment to accomplish research goals.
  • Publish research results in peer reviewed scientific or technical journals and present results at external conferences seminars and/or technical meetings.
  • Perform other duties as assigned.

Qualifications

  • PhD in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics, Operation Research, or related field.
  • Knowledge and experience in reinforcement learning, active learning, or stochastic control algorithms.
  • Experience in one or more of the following machine learning areas: deep learning, unsupervised feature learning, multimodal learning, and probabilistic graphical models.
  • Experience in the broad application of two or more higher-level programming languages such as Python, Java, Matlab, R or C/C++.
  • Experience with one or more deep learning libraries such as TensorFlow, Keras, Caffe or Theano, and experience with one or more deep reinforcement learning libraries such as rllab, keras-rl or OpenAI Gym.
  • Experience developing independent research projects, including publication of peer-reviewed literature.
  • Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
  • Initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.

Qualifications We Desire

 

  • Strong math background and experience with mathematical formulations of complex systems. Domain knowledge in biological sciences or cyber security.
  • Experience with a variety of deep reinforcement learning algorithms including experience with variational Bayesian methods, nonparametric Bayesian methods, and multi-agent systems.
  • Experience with utilizing simulation to model and analyze complex systems and experience with parallel computing and/or GPU computing.

Additional Information

Why Lawrence Livermore National Laboratory?

  • Included in 2021 Best Places to Work by Glassdoor!
  • Work for a premier innovative national Laboratory
  • Comprehensive Benefits Package
  • Flexible schedules (*depending on project needs)
  • Collaborative, creative, inclusive, and fun team environment

Learn more about our company, selection process, position types and security clearances by visiting our Career site

Security Clearance

LLNL is a Department of Energy (DOE) and National Nuclear Security Administration (NNSA) Laboratory.  Most positions will require a DOE L or Q clearance (please reference Security Clearance requirement).  If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing.  An L or Q clearance requires U.S. citizenship.  If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted.  For additional information please see DOE Order 472.2.

Equal Employment Opportunity

LLNL is an affirmative action and equal opportunity employer that values and hires a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.

Videos To Watch