The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and computational science expertise
With the coming Aurora exascale supercomputer, the ALCF has an opening for a postdoctoral position in developing in situ and in transit techniques for applying visualization, data analysis and machine learning methods to physics-based simulations. The successful candidate will contribute as a member of a dynamic, multidisciplinary team of computer scientists, applied mathematicians, and biomedical engineers developing innovative techniques that enable large-scale fluid dynamics simulations on next-generation high performance architectures. This project is in collaboration with the Randles Lab at Duke University and involves developing predictive, physics-based models of patient-specific blood flow.
- Leverage new and existing in situ technologies to incorporate visualization and analysis capabilities into large-scale fluid dynamics simulations.
- Research, design, and implement new machine learning-based tools that will accelerate training on large-scale fluid dynamics data sets.
- Contribute novel analysis and transformational ideas to support in situ analysis and ML-training for patient-specific blood flow modeling
- Solve abstract complex problems and/or ideas and convert them into usable algorithms and software
- Document research by publishing papers in peer-reviewed media and presenting papers within the HPC community and at conferences
- Contribute to group grant proposals, including proposal presentations and preparation of proposal that will provide future research opportunities in the field
- Participate in the establishment of future research directions
- Perform other duties as assigned
- Work in a collaborative multidisciplinary team environment to accomplish goals and interact with the team at ANL and Duke weekly
- Recent Ph.D. in biomedical engineering, computer science, applied mathematics, or a related field.
- Expertise in C/C++ in a UNIX environment
- Expertise using Python and/or other scripting languages
- Expertise in parallel programming and using MPI and OpenMP
- Effective verbal and written communication skills necessary to interact with a multi-disciplinary research team, author technical and scientific reports and papers, and deliver scientific presentations
- Demonstrated experience developing independent research and experience in the identification of complex problems and solutions in a creative and timely manner
- Experience in applying large-scale simulation to study biomedical phenomena
- Experience with scientific applications and numerical algorithms
- Experience with the lattice Boltzmann method for large-scale computational fluid dynamics
- Experience with applying machine learning to physics-based models
- Experience with the HARVEY software package
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