27 days old

Transport Reduced Order Modeling Postdoctoral Researcher Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

We have an opening for a Postdoctoral Researcher to conduct basic and applied research in surrogate/reduced order models of energy systems. You will work independently to both apply commercial tools and extend and develop efficient physics-based data-driven emulators in focus areas including energy storage/conversion and carbon storage/conversion. This position is in the Materials Engineering Division (MED) of the Engineering Directorate.

In this role you will 

  • Conduct research and development in the data-driven modeling and simulation of energy systems for energy storage/conversion, carbon storage/conversion, and advanced manufacturing.
  • Develop and analyze surrogate/reduced order models to describe the fluid mechanics, mass transfer, and chemical reactions describing energy systems (e.g., electrochemical reactors, flow batteries, fuel cells, capacitors, heat exchangers, absorbers, etc.).
  • Design, implement, and analyze computational techniques and tools in one or more of the above areas.
  • Employ and extend commercial, open-source, and LLNL codes to implement data-driven models and perform electrochemical simulations.
  • Work with engineers and scientists to identify and solve challenging simulation and modeling problems.
  • Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external 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.

  • PhD in engineering, applied mathematics, computational science, scientific machine learning or a related field.
  • Experience developing or modifying simulations using continuum (finite-element/ finite-volume) approaches for flowing reactive mass transfer systems (e.g., chemical reactors, electrochemical reactors, fuel cells, combustion, etc.).
  • Experience programming in machine learning software, such as PyTorch and TensorFlow.
  • Experience with commercial (e.g., COMSOL, Starccm+, Ansys/Fluent, etc.), opensource (e.g., OpenFOAM, FENICS, etc.), or other institutional codes.
  • Proficient verbal and written communication skills needed to effectively collaborate in a team environment, present and explain technical information, document work, prepare and present successful proposals and high-quality research papers, and provide advice to management.

Qualifications We Desire

  • Experience programming in C/C++ and scripting languages like Python/Matlab.
  • Experience developing continuum simulations for electrochemical systems.
  • Experience with averaging PDEs to describe flow and transport in porous materials.
  • Experience with massively parallel computing and one or more associated parallel programming interfaces, such as MPI, OpenMP, or CUDA.

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

This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.  

If you are selected and a security clearance is required, 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.  L and Q-level clearances require U.S. citizenship.  

If no security clearance is required, but 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 have an opening for a Postdoctoral Researcher to conduct basic and applied research in surrogate/reduced order models of energy systems. You will work independently to both apply commercial tools and extend and develop efficient physics-based data-driven emulators in focus areas including energy storage/conversion and carbon storage/conversion. This position is in the Materials Engineering Division (MED) of the Engineering Directorate.

In this role you will 

  • Conduct research and development in the data-driven modeling and simulation of energy systems for energy storage/conversion, carbon storage/conversion, and advanced manufacturing.
  • Develop and analyze surrogate/reduced order models to describe the fluid mechanics, mass transfer, and chemical reactions describing energy systems (e.g., electrochemical reactors, flow batteries, fuel cells, capacitors, heat exchangers, absorbers, etc.).
  • Design, implement, and analyze computational techniques and tools in one or more of the above areas.
  • Employ and extend commercial, open-source, and LLNL codes to implement data-driven models and perform electrochemical simulations.
  • Work with engineers and scientists to identify and solve challenging simulation and modeling problems.
  • Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external 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":{"title":"Qualifications","text":"
  • PhD in engineering, applied mathematics, computational science, scientific machine learning or a related field.
  • Experience developing or modifying simulations using continuum (finite-element/ finite-volume) approaches for flowing reactive mass transfer systems (e.g., chemical reactors, electrochemical reactors, fuel cells, combustion, etc.).
  • Experience programming in machine learning software, such as PyTorch and TensorFlow.
  • Experience with commercial (e.g., COMSOL, Starccm+, Ansys/Fluent, etc.), opensource (e.g., OpenFOAM, FENICS, etc.), or other institutional codes.
  • Proficient verbal and written communication skills needed to effectively collaborate in a team environment, present and explain technical information, document work, prepare and present successful proposals and high-quality research papers, and provide advice to management.

Qualifications We Desire

  • Experience programming in C/C++ and scripting languages like Python/Matlab.
  • Experience developing continuum simulations for electrochemical systems.
  • Experience with averaging PDEs to describe flow and transport in porous materials.
  • Experience with massively parallel computing and one or more associated parallel programming interfaces, such as MPI, OpenMP, or CUDA.

Categories

Posted: 2022-06-09 Expires: 2022-07-09

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Transport Reduced Order Modeling Postdoctoral Researcher Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

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