Postdoctoral Appointee-Developing an Exascale MuPhFASa (Multi Phase Flow Adaptive Simulator)

Argonne National Laboratory; United States; On-site; USD 72,879 - 121,465; Full time

Job summary

Argonne National Laboratory is seeking a postdoctoral appointee to develop advanced computational fluid dynamic tools aimed at enhancing accessibility to exascale computing. The successful candidate will design a massively parallel solver for simulations on the Aurora supercomputer, leveraging methods such as the lattice Boltzmann method.

This role contributes significantly to the computational science community and aims to refine advanced workloads. A Ph.D. in a related discipline and strong programming skills in C, C++, or Python are required.

Qualifications

• Recent Ph.D. in a relevant field.
• Experience in CFD development using mesh-based scientific codes.
• Expertise in the lattice Boltzmann method with publications.

Responsibilities

• Develop computational fluid dynamic tools that enhance exascale computing accessibility.
• Design a massively parallel solver for the Aurora supercomputer.
• Create flow/geometry-aware refinement strategies.

Skills

Numerical methods

CFD development

High performance computing (HPC)

C, C++, and/or Python proficiency

Strong written and oral communication skills

Education

Ph.D. in mechanical, aerospace, chemical engineering, applied mathematics, or related discipline

Tools

MPI

CUDA

SYCL

HIP

OpenMP

AMReX

Job description

Position Overview

The Argonne Leadership Computing Facility (ALCF) and the Computational Science (CPS) Division are seeking a postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will design and implement a massively parallel solver capable of running simulations on the Aurora supercomputer, using AMReX and the lattice Boltzmann method (LBM).

The role involves creating flow/geometry‑aware refinement strategies that go beyond traditional error estimators, developing physics‑based adaptation algorithms to predict where refinement will be most beneficial, and building an integrated framework to explore advanced workloads such as simulations with in‑situ visualization and, potentially, machine learning integration.

This work will inform future ALCF platform procurement decisions and contribute to the broader computational science community.

Position Requirements

• Recent or soon‑to‑be completed Ph.D. (typically within the last 5 years) in mechanical, aerospace, chemical engineering, applied mathematics, or a related discipline.
• Experience in numerical methods and CFD development using mesh‑based scientific codes.
• Expertise in the lattice Boltzmann method (LBM) as evidenced by publications.
• High performance computing (HPC) experience in code development with parallel programming techniques using MPI.
• Proficiency in writing code with C, C++ and/or Python.
• Strong written and oral communication skills.
• Alignment with Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Desired Skills

• Experience with two‑phase/multi‑phase flows as evidenced by publications.
• Experience programming GPUs with CUDA, SYCL, HIP, or OpenMP.
• Experience using and developing code with AMReX.
• Experience in performance engineering to improve code scalability and reduce time‑to‑solution.

Equal Employment Opportunity

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Salary range: $72,879.00–$121,465.00 (actual salary will be based on qualifications, responsibilities, and market conditions). Additional benefits are provided as part of the total rewards package.