Argonne National Laboratory Postdoctoral Appointee: Multi-Phase Flows & Machine Learning in Lemont, Illinois
Postdoctoral Appointee: Multi-Phase Flows & Machine Learning
Requisition Number: 408568 Location: Lemont, IL
Functional Area: Research and Development Division: ES-Energy Systems
Employment Category: Temporary 6 Months or Greater Education Required: Doctorate Degree
Level (Grade): 700 Shift: 8:30 - 5:00 Share: Facebook LinkedIn Twitter
Perform Computational Fluid Dynamics (CFD) simulations of turbulent multiphase flow and erosion development inside fuel injectors using High-Performance Computing (HPC) tools. Build multi-fidelity models for two-phase flows by fusing data from state-of-the-art experimental measurements with high-fidelity simulation predictions. Improve the accuracy of two-phase flow models for: 1) phase change of multi-component mixtures, and 2) dynamic coupling of nozzle flow with spray simulations for both diesel and gasoline direct injection applications.
The successful candidate’s research will involve synergetic collaborations with a multidisciplinary team involving engine modelers and experimentalists, and computational scientists to enhance the predictive capability of engine modeling codes.
- Candidates should have a Ph.D. in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a related discipline.
- Considerable knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase and/or multiphase flow applications, turbulence modeling, internal combustion engine theory and operation, and parallel scientific computing is required.
- Knowledge of deep learning (in TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of big data, and parallel scientific computing is desired.
- Experience in modeling and simulation of three-dimensional two-phase and/or multiphase flow applications using CFD codes (e.g. CONVERGE, OpenFoam, etc.) is desired.
- Experience in geometry manipulation with computer-aided design software is desired.
- Knowledge of large scientific code management and optimization is desirable.
- Good collaborative skills, including the ability to work well with other divisions, laboratories, and universities.
- Strong oral and 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 is desirable.
As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.