The Advanced Propulsion and Power department at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s research will involve synergistic collaborations with a multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes.

The prospective postdoctoral appointee will perform multi-physics and multi-scale CFD simulations of internal combustion engines involving modeling of multi-phase flows, turbulent combustion, heat transfer, and emissions by further developing commercial/in-house codes and making use of high-performance computing (HPC) tools.

• Develop accurate, computationally efficient physics-based and data-driven CFD models which the candidate will use to simulate the chain of physics and chemistry involved with fuel injection, spray-wall interaction, fuel-air mixing, turbulent combustion, abnormal combustion (e.g., knock, pre-ignition), and emissions formation.
• Perform CFD simulations of fuel injection, mixing, and combustion processes for heavy-duty internal combustion engines using High-Performance Computing.
• Improve the computational efficiency and accuracy of existing physics-based and data-driven fuel injection and spray models and integrate them into simulations of propulsion systems.
• Perform high-fidelity reacting flow simulations that involve both conventional and alternative fuels.
• Work as a part of a multidisciplinary team involving experimentalists, CFD experts, AI/ML specialists, and computational scientists to enable cutting-edge CFD modeling & simulations on HPC clusters and next-generation supercomputing architectures.
• Collaborate with other staff scientists and postdocs in the group.
• Work effectively to meet priorities and deadlines set by the research sponsor(s).

Position Requirements

• Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical, aerospace, energy engineering or a closely related discipline.
• Experience in modeling & simulation of three-dimensional multiphase turbulent reacting flow applications including internal combustion engines and/or gas turbine engines using 3-D CFD codes (e.g., CONVERGE, OpenFOAM, Fidelity Charles, Ansys Fluent, etc.).
• Understanding of fuel sprays, spray-wall interaction, turbulence, chemical kinetics, reacting flow physics, and turbulent combustion modeling.
• Knowledge of fuel properties and their effects on fluid behavior in reacting and non-reacting flow applications.
• Understanding of combustion theory and modeling applied to combustion engines and other propulsion systems.
• Ability to demonstrate good collaborative skills, including the ability to work well with other divisions, laboratories, and universities.
• Skilled in communication at all levels of the organization.
• The successful candidate is expected to present and publish results in peer-reviewed technical reports and journal articles.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.
• This position requires an on-site presence at the Argonne campus in Lemont, Illinois.

Preferred Qualifications:

• Knowledge of and experience with modeling conventional and alternative fuels.
• Experience in geometry manipulation with computer-aided design software.
• Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, as well as parallel scientific computing.
• Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) and agentic AI for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and parallel scientific computing.
• Experience in carrying out research tasks with industry partners.
• Experience in interdisciplinary collaborative research.

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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