1+ months

Deep Learning and Climate Predictions Postdoctoral Research Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

We are seeking a Postdoctoral Research Staff Member who is excited to work at the interface of climate science, data science and machine learning. You will work with a team of experts in climate science, machine learning, data science, and computational science to develop and test new approaches to challenging problems. Your contributions have the potential to make enormous societal impacts by predicting the impacts of climate change on the availability of natural resources and on infrastructure over the coming decades. Your work will also help to improve next generation climate models. This position will be in the Atmospheric, Earth & Energy Division.

In this role you will

  • Conduct research and development by applying state-of-the-art machine learning approaches to improve climate model predictions on seasonal to decadal time scales.
  • Train, test, and validate deep learning models for bias correction and downscaling of climate simulations.
  • Gather and process large climate data sets for training and testing of deep neural networks.
  • Document research by publishing papers in peer-reviewed journals and presenting technical results at scientific conferences.
  • Work independently and interact with a broad spectrum of scientists internally and externally.
  • Travel as required to coordinate with collaborators.
  • Perform other duties as assigned.

  • PhD in climate science, atmospheric science, environmental engineering, or a related field.
  • Experience in convolutional neural network (CNN) architectures and statistical techniques.
  • Experience in handling and analyzing large datasets.
  • Proficient in one or more computer languages used in data science (Python, R, or MATLAB).
  • Ability to conduct high quality, independent research.
  • Proficient verbal and written communication skills as evidenced by published results and presentations.
  • Experience collaborating effectively with a team of scientists of diverse backgrounds
  • Ability to tavel as needed.

Qualifications we desire

  • Experience with using Generative Adversarial Networks (GANs), Bayesian deep learning and domain adaptation.
  • Experience with working on modern observations and climate model simulations.

All your information will be kept confidential according to EEO guidelines.

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years. Eligible candidates are those who have been awarded a PhD at time of hire date.

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Why Lawrence Livermore National Laboratory?

  • Included in 2022 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.

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COVID-19 Vaccination Mandate

LLNL demonstrates its commitment to public safety by requiring that all new Laboratory employees be immunized against COVID-19 unless granted an accommodation under applicable state or federal law. This requirement will apply to all new hires including those who will be working on site, as well as those who will be teleworking.

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Security Clearance

None required. However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check. (This process does not apply to foreign nationals.) For additional information, please see DOE Order 472.2.

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Pre-Employment Drug Test

External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

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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.

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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.


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.

"},"jobDescription":{"title":"Job Description","text":"

We are seeking a Postdoctoral Research Staff Member who is excited to work at the interface of climate science, data science and machine learning. You will work with a team of experts in climate science, machine learning, data science, and computational science to develop and test new approaches to challenging problems. Your contributions have the potential to make enormous societal impacts by predicting the impacts of climate change on the availability of natural resources and on infrastructure over the coming decades. Your work will also help to improve next generation climate models. This position will be in the Atmospheric, Earth & Energy Division.

In this role you will

  • Conduct research and development by applying state-of-the-art machine learning approaches to improve climate model predictions on seasonal to decadal time scales.
  • Train, test, and validate deep learning models for bias correction and downscaling of climate simulations.
  • Gather and process large climate data sets for training and testing of deep neural networks.
  • Document research by publishing papers in peer-reviewed journals and presenting technical results at scientific conferences.
  • Work independently and interact with a broad spectrum of scientists internally and externally.
  • Travel as required to coordinate with collaborators.
  • Perform other duties as assigned.
"},"qualifications":{"title":"Qualifications","text":"
  • PhD in climate science, atmospheric science, environmental engineering, or a related field.
  • Experience in convolutional neural network (CNN) architectures and statistical techniques.
  • Experience in handling and analyzing large datasets.
  • Proficient in one or more computer languages used in data science (Python, R, or MATLAB).
  • Ability to conduct high quality, independent research.
  • Proficient verbal and written communication skills as evidenced by published results and presentations.
  • Experience collaborating effectively with a team of scientists of diverse backgrounds
  • Ability to tavel as needed.

Qualifications we desire

  • Experience with using Generative Adversarial Networks (GANs), Bayesian deep learning and domain adaptation.
  • Experience with working on modern observations and climate model simulations.

Categories

Posted: 2022-02-08 Expires: 2022-06-09

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Deep Learning and Climate Predictions Postdoctoral Research Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

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