We invite you to apply for a postdoctoral position at the Argonne Leadership Computing Facility (ALCF). You will research and develop a scientific workflow targeting our forthcoming exascale supercomputer, Aurora, which will couple machine learning with electronic structure calculations to identify candidate materials for solar cells. The position is under ALCF’s Aurora Early Science Program, which combines the efforts of multi-institutional investigator teams with ALCF staff and Aurora vendor applications experts.

The postdoc will be located at ALCF and will be dedicated to the ESP project "Many-Body Perturbation Theory Meets Machine Learning to Discover singlet Fission Materials", led by Noa Marom (Carnegie Mellon University) and co-PIs Jack Deslippe (Lawrence Berkeley National Lab), Luca Ghiringhelli (FHI-Berlin), and Barnabas Poczos (Carnegie Mellon University). In addition to the project PIs and extended project team, you will work with a computational scientist (Catalyst) and a data scientist at ALCF. You will have a rare chance to learn and exploit some of the world’s largest supercomputers, and use state-of-the-art techniques on our current and next-generation supercomputing systems to help advance significant scientific problems.

Position Requirements:

Ideal candidates are expected to have:

• PhD + 0-3 years in computational sciences including chemistry, physics, materials engineering, or in a related field.
• Comprehensive experience programming in one or more programming languages such as Fortran, C, C++, and Python.
• Considerable knowledge of parallel algorithms, distributed memory architectures, and parallel performance evaluation of domain specific implementations.
• Candidate should have the ability to create, maintain, and support high-quality software using a revision control systems; e.g. SVN or GIT
• Good communication skills both verbal and written.
• Considerable independent judgment and critical thinking.

Desirable Knowledge and Skills:

• Considerable knowledge of density functional theory (DFT), and excited state theory in particular GW and the Bethe-Salpeter Equation (BSE).
• Experience with data science/machine learning/deep learning methodologies and software frameworks.
• Experience with databases.
• Experience with workflow automation.
• Experience and skills in interdisciplinary teams involving mathematicians, computer scientists, and discipline scientists.
• Collaborative skills including the ability to work well with other laboratories and universities, supercomputer centers, and industry.
• Leadership, initiative, and creativity.
• Strong publication record, preferably including first-author publications.
• Experience with scientific applications that scale to several thousand processes on massively parallel platforms.

The mission of the Argonne Leadership Computing Facility (ALCF) 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 expertise.

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 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, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

To apply go to: https://careers.peopleclick.com/careerscp/client_argonnelab/post_doc/jobDetails.do?functionName=getJobDetail&jobPostId=7481&localeCode=en-us