Postdoctoral Appointee – CFD and Machine Learning for Manufacturing Process Control - Military Veterans

at Argonne National Laboratory

Argonne, Wisconsin

Develop magnetohydrodynamics model within multiphase CFD framework to simulate electrospinning process for nanofiber fabrication. Formulate a multi-fidelity machine learning framework to learn electrospinning parameters based on CFD predictions and experimental measurements. Use multi-fidelity model to optimize process parameters for electrospun nanofiber fabrication.

The successful candidate’s research will involve synergistic collaborations with a multidisciplinary team involving multiphase flow modelers, material scientists, and machine learning experts to develop new simulation capabilities and improve process planning for electrospinning experiments

  • Develop and implement a magnetohydrodynamics model that can couple with interface capturing methods for multiphase flows

  • Perform high-fidelity simulations of electrospinning process using high performance computing resources

  • Develop machine learning frameworks for optimization of manufacturing processes

  • Work as a part of a multidisciplinary team involving experimentalists, Computational Fluid Dynamics experts and computational scientists to run the simulations using the next generation supercomputing architectures.

  • Present and publish results in peer reviewed society technical reports and journal articles.

Position Requirements

  • Ph.D. in mechanical/aerospace engineering, computer/data science, applied mathematics, chemical engineering, or a related discipline. 

  • Demonstrated background and experience in model development for computational fluid dynamics codes (e.g. OpenFOAM, ANSYS, COMSOL, CONVERGE)

  • Experience in development of statistical and machine learning algorithms and software (in TensorFlow, PyTorch, Julia, etc.), management and analysis of big data, and parallel scientific computing is required.

  • Understanding of electrostatic atomization, multiphase flow physics, interface-capturing methods, and process optimization is desired. Expertise in the development and application of machine learning tools in one or more of these areas is a plus.

  • Experience in simulation of multiphase flows in manufacturing processes (e.g. electrospinning, flame spray pyrolysis, melt-blown process etc.) using CFD codes (e.g. OpenFOAM, ANSYS, COMSOL, CONVERGE) is desired.

  • Good knowledge of large scientific code management and optimization is desirable.

  • Collaborative skills, including the ability to work well with other divisions, laboratories, and universities.

  • Ability to demonstrate strong written and oral communication skills at all levels of the organization.

  • A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. 

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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, gender expression, 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 Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.  

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis.  Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements.  Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

Please note that all Argonne employees are required to be vaccinated against COVID-19. All successful applicants will be required to provide their COVID-19 vaccination verification as a condition of employment, subject to limited legally recognized exemptions to COVID-19 vaccination.

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