Postdoctoral Appointee – Exascale Molecular Simulations
| Company | Argonne National Laboratory |
|---|---|
| Location | Woodridge, IL, USA |
| Salary | $70758 – $110379.55 |
| Type | Full-Time |
| Degrees | PhD |
| Experience Level | Junior |
Requirements
- Ph.D. (completed within the last 0-5 years) or equivalent experience in a computational science discipline, computer science, or in a related field
- Strong programming skills in one or more scientific programming language, such as C++ and Python
- Experience in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP
- Experience with scientific computing and software development on HPC systems
- Ability to conduct independent research and demonstrated publication record in peer-reviewed journals and conferences
- The successful candidate will be expected to work with and contribute to open-source projects and community-driven initiatives within computational science
- Effective communication skills, both verbal and written, for effective collaboration with interdisciplinary teams and clear presentation of complex technical information
- Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
Responsibilities
- Enhancing the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis, and use of novel architectural features
- Working on the development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations
- Collaborating extensively across the computing divisions as well as with industrial and university collaborations and various experimental and computational groups in the Center for Nanoscale Materials (CNM) and Material Science Division (MSD) at Argonne
Preferred Qualifications
- Experience with large-scale molecular dynamics (MD) simulations using software, such as LAMMPS, and machine-learned potentials
- Experience in GPU programming with Kokkos
- An understanding of computer architecture and experience in the analysis and improvement of applications performance