The Multiphysics Computations Group (MPC) at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s research will involve synergetic collaborations with a multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists with computational fluid dynamics (CFD) and reduced-order modeling (ROM) expertise, with the goal of enhancing predictive capability and scaling of multi-scale and multi-physics simulation codes.

The prospective postdoctoral appointee with perform multi-physics and multi-scale CFD simulations of complex systems involving modeling of multiphase flows, heat transfer, external aerodynamics, and low-carbon propulsive systems by further developing commercial/in-house codes and high-performance computing (HPC). In addition, the prospective postdoctoral appointee will develop reduced-complexity models for accurate prediction of fluid dynamical systems for the purposes of increasing computational efficiency, developing efficient data processing, and extracting relevant physical information and dynamics for increased understanding and utilization.

Position Requirements

Candidates must have a Ph.D. in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a related discipline.

Knowledge of multi-dimensional code development for CFD and parallel scientific computing is required.

Experience in modeling and simulation of three-dimensional multiphase flow applications using 3-D CFD codes is required.

Expertise in fluid dynamics and heat and mass transfer is required.

Experience in modeling and simulation of external aerodynamics for aircraft systems is desirable.

Knowledge of reduced-complexity modeling (physics-based and/or data-driven), and dynamical systems modeling is desirable.

Knowledge of large scientific code management and optimization is desirable.

Demonstrate good collaborative skills, including the ability to work well with other divisions, laboratories, and universities.

Skilled in oral and written communications at all levels of the organization.

The successful candidate is expected to present and publish results in peer reviewed society technical reports and journal articles.

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 modeling refrigerants for HVAC applications.

Experience in leveraging volume-of-fluid (VOF) modeling with phase-change for CFD desirable.

Experience in modeling heat pump components and HVAC components/systems.

Experience in geometry optimization with computer-aided design software.

Knowledge of machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and parallel scientific computing.

Experience in interdisciplinary collaborative research.

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