Postdoctoral Appointee - Scientific Machine Learning and Uncertainty Quantification
- Argonne National Laboratory
- Job Number: 7151545 (Ref #: 415971)
- Posting Date: Jun 2, 2023
- Application Deadline: Open Until Filled
The Laboratory for Applied Mathematics, Numerical Software, and Statistics (LANS) and the Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invite candidates to apply for a postdoctoral position in the area of scientific machine learning and uncertainty quantification.
The position will address software/algorithm development and/or theory in areas of interest to the applied mathematics, numerical software, and statistics group.
The successful candidate will have the opportunity to carry out simulations on some of the world's fastest supercomputers and collaborate with other computer scientists and mathematicians at the MCS division.
For more information on the applied mathematics, numerical software, and statistics group at Argonne, see https://www.anl.gov/mcs/lans
- Recent or soon-to-completed PhD (typically within the last 0-3 years) in applied mathematics, statistics, computer science, industrial engineering or related field
- Expertise in two or more of the following areas: nonlinear optimization, uncertainty quantification, scientific machine learning (SciML), stochastic optimization, statistics, and numerical solution of partial differential equations
- Knowledgeable in one or more of the following areas: software development in SciML, software development in numerical optimization, statistics, and/or development of large-scale stochastic control algorithms.
- Proficient in one scientific programming language (e.g., C, C++, Fortran, Python, or Julia) are also required
- Experience with Julia, Python, parallel computing, large-scale computational science, and simulation of networked physical systems is a plus
- Ability to model Argonne's core values of impact, respect, integrity, safety and teamwork
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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