Postdoctoral Appointee - Scientific Machine Learning - Argonne National Laboratory
Lemont, IL
About the Job
Argonne’s Leadership Computing Facility (ALCF) and Mathematics and Computer Science Division (MCS) is looking for a Postdoctoral appointee working at the intersection of scientific machine learning SciML and large-scale simulation codes.
The Postdoctoral appointee will engage with projects that directly integrate machine learning (ML) components into partial differential equations (PDE)-based simulations, allowing ML algorithms to function as part of the simulation framework. This role involves coupling simulations with ML, enabling simultaneous training of these components within the simulation environment. The goal is to significantly accelerate traditional PDE-based simulations, particularly in fluid dynamics and turbulent flows.
As a Postdoc in ALCF/MCS, you will design ML kernels as local operators, embedding them within large-scale simulation codes. The work will also explore the use of advanced solvers like PETSc (www.petsc.org) for these developments, expanding their SciML applicability to a wider range of scientific applications. The incumbent will collaborate with scientists from multiple domains to enhance simulation speeds and accuracy through ML techniques.
Position Requirements
Required skills and qualifications:
+ Ph.D. (completed within the last 0-5 years) in applied mathematics, physics, computer science, mechanical engineering, aerospace engineering, or similar fields.
+ Experience contributing to scientific software in C, C++, Python, or comparable languages.
+ Strong background in computational fluid dynamics (CFD), machine learning (ML), and high-performance computing (HPC).
+ Collaborative skills include working well with other scientists, divisions, laboratories, and universities.
+ Effective oral and written communication skills with all levels of the organization.
+ Ability to model Argonne’s core values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred skills and qualifications:
+ Demonstrated experience with parallel algorithms, for example, using frameworks like MPI, OpenMP, CUDA, Kokkos, SYCL, or similar.
+ Demonstrated experience running applications at scale (> 50 computing nodes). This could be AI training, simulation, or data analytics.
+ Experience with PETSc (or other scientific libraries used as external solvers) and its integration with ML frameworks such as PyTorch.
+ Knowledge of subgrid-scale models and closure models.
+ Experience with computing gradients for PDE-constrained optimization problems, adjoint calculations and backpropagation.
+ Ability to address dynamic stability issues of hybrid systems during training and operational phases.
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._
The Postdoctoral appointee will engage with projects that directly integrate machine learning (ML) components into partial differential equations (PDE)-based simulations, allowing ML algorithms to function as part of the simulation framework. This role involves coupling simulations with ML, enabling simultaneous training of these components within the simulation environment. The goal is to significantly accelerate traditional PDE-based simulations, particularly in fluid dynamics and turbulent flows.
As a Postdoc in ALCF/MCS, you will design ML kernels as local operators, embedding them within large-scale simulation codes. The work will also explore the use of advanced solvers like PETSc (www.petsc.org) for these developments, expanding their SciML applicability to a wider range of scientific applications. The incumbent will collaborate with scientists from multiple domains to enhance simulation speeds and accuracy through ML techniques.
Position Requirements
Required skills and qualifications:
+ Ph.D. (completed within the last 0-5 years) in applied mathematics, physics, computer science, mechanical engineering, aerospace engineering, or similar fields.
+ Experience contributing to scientific software in C, C++, Python, or comparable languages.
+ Strong background in computational fluid dynamics (CFD), machine learning (ML), and high-performance computing (HPC).
+ Collaborative skills include working well with other scientists, divisions, laboratories, and universities.
+ Effective oral and written communication skills with all levels of the organization.
+ Ability to model Argonne’s core values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred skills and qualifications:
+ Demonstrated experience with parallel algorithms, for example, using frameworks like MPI, OpenMP, CUDA, Kokkos, SYCL, or similar.
+ Demonstrated experience running applications at scale (> 50 computing nodes). This could be AI training, simulation, or data analytics.
+ Experience with PETSc (or other scientific libraries used as external solvers) and its integration with ML frameworks such as PyTorch.
+ Knowledge of subgrid-scale models and closure models.
+ Experience with computing gradients for PDE-constrained optimization problems, adjoint calculations and backpropagation.
+ Ability to address dynamic stability issues of hybrid systems during training and operational phases.
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._
Source : Argonne National Laboratory
