The Multiphysics Computation Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s research will involve synergistic collaborations with a multidisciplinary team involving engine modelers, CFD and AI/ML experts, and computational scientists to enhance the predictive capability and scalability of multi-scale and multi-physics simulation codes.
Develop turbulent combustion modeling approaches augmented with machine learning (ML) for computational fluid dynamics (CFD) simulations of supersonic turbulent reacting flows in rotating detonation engines (RDEs) operating on low/zero-carbon fuels. Demonstrate the capability of novel CFD-ML frameworks to speed up full-scale simulations of RDEs. Import and accelerate frameworks on leadership class supercomputing platforms.
Ph.D. in mechanical/aerospace engineering, chemical engineering, or a related discipline.
Understanding of chemical kinetics, compressible reacting flows, and turbulent combustion modeling is required. Expertise in the development and application of machine learning tools in one or more of these areas is a plus.
Background and expertise in CFD modeling of turbulent reacting flows within energy conversion systems (e.g., internal combustion engines, gas turbine combustors, detonation engines, etc.) using CFD solvers (e.g., CONVERGE, OpenFOAM, Fluent, etc.) on large-scale HPC platforms is required.
Demonstrated background and experience in the development of machine learning algorithms and software (in TensorFlow, PyTorch, Julia, etc.) for surrogate-assisted modeling, management and analysis of big data, and parallel scientific computing is highly desirable.
Collaborative skills, including the ability to work well with other divisions, laboratories, and universities.
Ability to demonstrate strong written and oral communication skills.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
What will put you ahead:
Experience in interdisciplinary collaborative research.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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