We have an opening for a Postdoctoral Researcher with a strong computational science background to conduct research aimed at constructing an efficient reduced order model for simple/complex physics simulation codes. You will develop and implement model order reduction approaches for hydrodynamics to address performance of the reduced order models in terms of accuracy and speedup. This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Directorate.
In this role you will
- Conduct research in and development of one or more of the following areas: computational science, numerical methods, reduced order modeling, linear algebra, nonlinear optimization, compression, finite element methods, finite volume methods, high-performance computing, machine learning, and statistics.
- Design, implement, and analyze computational techniques and tools in one or more of the above areas.
- Organize and analyze physical simulations and formulate conclusions advancing our knowledge of reduced order modeling.
- 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.
- Recent PhD in computational science/mechanics or related field.
- Experience in one or more of the following computational science areas: machine learning, reduced order modeling, fluid/solid mechanics/heat transfer, finite elements, finite volumes, and spectral methods.
- Demonstrated programming ability in C, C++, FORTRAN, Python, or similar languages.
- Demonstrated ability to improve physics simulation through development of computational tools and their utilization for advanced modeling of complex phenomenon.
- Demonstrated ability to develop independent research projects as demonstrated through publication of peer-reviewed literature.
- Demonstrated proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
- Demonstrated initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.
Qualifications We Desire
- Experience in Python, MATLAB, PyTorch, TensorFlow, data parsing/preprocessing tools, and/or data analysis tools.
- Previous exposure to various data-driven physical simulations, such as reduced order modeling.
- Previous exposure to various physical simulations, e.g., hydrodynamics and transport.
Lawrence Livermore National Laboratory
Lawrence Livermore National Laboratory (LLNL) is a research institution, It's primary mission is to strengthen the United States' security by developing and applying advanced science, engineering, and technology in the areas of national security, nuclear deterrence, and stockpile stewardship. The laboratory is also involved in fundamental research and development in fields such as energy, biomedicine, materials science, and environmental science.
No. of Employees
Livermore, CA, USA
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