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Los Alamos National Laboratory · 3 days ago

Turbulence Modeling Post-Doctoral Research Associate

Los Alamos, NM

Full-time

Onsite

New Grad, Entry Level

Los Alamos National Laboratory is a multidisciplinary research institution engaged in strategic science on behalf of national security. They are seeking a highly motivated postdoctoral researcher in modeling and analysis of complex turbulent flows, focusing on developing new methodologies and performing simulations on high-performance computing platforms.

Security

Growth Opportunities

No H1B

U.S. Citizen Only

Responsibilities

Collaborate with a team to develop new methodologies for turbulence closures in complex flows, leveraging theoretical foundations, advanced machine learning algorithms, and large-scale simulations

Perform simulations on cutting-edge, high-performance computing platforms

Work closely with colleagues across LANL to advance turbulence model development and validation

Have opportunities to publish in leading peer-reviewed journals and present research at international conferences

Qualification

Turbulence modelingHigh-performance computingScientific programmingMachine learningNumerical methodsResearch excellenceData analysisCommunication skills

Required

A Ph.D. in Mechanical Engineering or a closely related field, earned within the last 5 years

Demonstrated research excellence, as evidenced by a strong publication record in one or more of the following areas: Turbulence and turbulent mixing, compressible flows, numerical methods, data-driven reduced order models, physics constrained machine learning for scientific applications, and high-performance computing with MPI and large-scale simulations

Proficiency in scientific programming, preferably in Python, Fortran, or C/C++. Strong communication skills, both written and oral, demonstrated through publications and the cover letter

Work Location: The work location for this position is onsite and located in Los Alamos, NM

Preferred

Experience in Direct Numerical Simulation (DNS), subgrid modeling, Reynolds-Averaged Navier-Stokes (RANS), and Probability Density Function (PDF) methods for compressible flows

Familiarity with large-scale data analysis and the application of modern machine learning techniques to scientific computing

Background in turbulence model development and validation against experimental or simulation data