Postdoctoral Appointee- Turbulence Simulation and Modeling

Argonne National Laboratory

Argonne, IL

Job posting number: #7121766 (Ref:414751)

Posted: January 21, 2023

Application Deadline: Open Until Filled

Job Description

A joint collaboration between Argonne's Mathematics and Computer Science Division and the Barcelona Supercomputing Center (BSC) seeks a Postdoctoral Appointee to fill a two year position that will involve one year of work at Argonne National Laboratory near Chicagoland and one year at BSC in Madrid.

The work involves exploration of large-eddy simulation (LES) of turbulent atmospheric flows with wall models using variational-based codes such as the spectral element code Nek5000/RS and the finite element code Alya. Accurate peta- and exascale simulations of the atmospheric boundary layer will feed into reduced order models based on classic POD or machine learning approaches. The position will require wrti2ng of reports and articles based on research findings.

Position Requirements

  • A recent or soon-to-be completed PhD degree (typically within the last 0-3 years)
  • Experience in computational fluid dynamics, LES, turbulence modeling, and high performance computing.
  • Familiarity with parallel computing, large-scale visualization, and interrogation and data analysis techniques for turbulent flow data.
  • Experience with Nek5000/RS and Alya a plus. Experience with Fortran, C, C++, Cuda also a plus.
  • Ability to model Argonne's core values of impact, respect, safety, integrity and teamwork.

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's 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.


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