Postdoctoral Appointee – Scalable Simulation and AI/ML Workflows
Argonne National Laboratory
Lemont, USA
Job posting number: #7265232 (Ref:418611)
Posted: July 23, 2024
Job Description
The Argonne Leadership Computing Facility (ALCF) is seeking a Postdoctoral Appointee to perform research and development of scalable workflow tools for coupling traditional HPC simulations with AI/ML.
This postdoctoral appointee will build upon work already being done at the Facility to enable such workflows and will contribute to the development and benchmarking of scalable solutions integrating parallel simulations with AI/ML distributed training and inferencing on leadership systems, such as the Aurora exascale supercomputer. Potential project areas include online training of robust AI/ML surrogates, AI driven workflows for efficient design space exploration and parameter optimization, cross-system workflows incorporating traditional HPC systems with novel AI accelerators, and developing performance benchmarks for evaluating hardware on current and future HPC systems.
In this role, you can expect to:
- Collaborate with scientists and researchers across multiple scientific domains and Argonne divisions, as well as other national labs and industry partners
- Contribute to open-source software used on platforms ranging from laptops to the world’s largest supercomputers
- Present research findings through journal publications, conference presentations, workshops, and other means
- Refine their skills and knowledge related to HPC and AI for science, including large scale distributed training, active learning and fine-tuning
Position Requirements
Required skills and qualifications:
- Ph.D. (completed within the last 0-5 years or soon-to-be-completed in 2024) in computer science, applied mathematics, physics, chemistry, or similar fields
- Experience contributing to scientific software in C++, Fortran, Python, or comparable languages
- Familiarity with building and deploying software on HPC systems
- Effective written and oral communication skills
- Ability to work individually and collaboratively as part of a team
- Ability to model Argonne’s core values: Impact, Safety, Respect, Integrity, and Teamwork
Preferred skills and qualifications:
- Demonstrated experience with parallel scientific computing (e.g., MPI, OpenMP, CUDA, etc.) or high-throughput computing on leadership class HPC systems
- Experience with machine learning methods and frameworks (e.g., TensorFlow, PyTorch, Jax, etc.), especially applied to scientific problems
- Experience with distributed ML training and inference tasks on multiple GPUs
- Experience with high-throughput computing or running large job ensembles on HPC systems
Job Family
Postdoctoral FamilyJob Profile
Postdoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
Full timeAs 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.
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Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
