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

Argonne National Laboratory; Lemont (IL); On-site; USD 72,879 - 121,465; Full time

Benefits offered by this job

Comprehensive benefits

Job summary

Argonne National Laboratory is seeking a Postdoctoral Appointee to develop computational fluid dynamic tools for exascale computing. The candidate will work on a massively parallel solver for the Aurora supercomputer using AMReX and lattice Boltzmann method.

This role requires a recent Ph.D. and experience in numerical methods, high-performance computing, and programming. The expected salary range is $72,879.00–$121,465.00 with comprehensive benefits.

Qualifications

• Recent or soon-to-be completed Ph.D. in a related discipline.
• Experience in numerical methods and CFD development.
• Expertise in the lattice Boltzmann method with publications.

Responsibilities

• Develop a massively parallel CFD solver for the Aurora supercomputer.
• Design and implement flow/geometry-aware refinement strategies.
• Build an integrated framework for advanced workloads.

Skills

Numerical methods and CFD development

High-performance computing experience

Proficiency in C, C++ and/or Python

Strong written and oral communication skills

Education

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

Tools

CUDA

SYCL

HIP

OpenMP

MPI

Job description

Overview

The Argonne Leadership Computing Facility (ALCF) accelerates scientific discoveries by providing world‑leading supercomputing resources. The Computational Science (CPS) Division develops advanced modeling and simulation capabilities integrated with mathematics, computer science, domain science, and advanced computing architectures. ALCF and CPS seek a postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to broader users. The candidate will design a massively parallel solver for the Aurora supercomputer using AMReX and the lattice Boltzmann method (LBM), developing physics‑based refinement strategies and an integrated framework for advanced workloads, including in‑situ visualization and potential machine‑learning integration. This work will inform future platform procurement decisions.

Responsibilities

• Develop a massively parallel CFD solver based on LBM and AMReX for the Aurora supercomputer.
• Design and implement flow/geometry‑aware refinement strategies that exceed traditional error estimators.
• Build an integrated framework for advanced workloads, such as in‑situ visualization and optional machine‑learning integration.
• Provide research outputs that inform future ALCF platform procurement.

Position Requirements

• Recent or soon‑to‑be completed Ph.D. (within the last five 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, evidenced by publications.
• High‑performance computing experience; parallel programming with MPI.
• Proficiency in C, C++ and/or Python.
• Strong written and oral communication skills.
• Demonstrated 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 with AMReX code development.
• Performance engineering experience to improve code scalability and reduce time‑to‑solution.

Job Family / Type

Postdoctoral Appointee – Long‑Term (Fixed Term), Full‑time.

Compensation and Benefits

The expected hiring range for this position is $72,879.00–$121,465.00. The total rewards package includes comprehensive benefits.

EEO Statement

Argonne National Laboratory is an equal employment opportunity employer and is committed to providing a diverse and inclusive workplace. All qualified applicants will be considered regardless of race, color, religion, sex, national origin, disability, or genetic information.