We are seeking an Assistant Computational Mathematician to join our Mathematics and Computer Science (MCS) division at Argonne National Laboratory. In this role, you can expect to:

• Research and development of novel methodologies and scalable algorithms for uncertainty quantification, data assimilation, and optimal experimental design for large-scale scientific physical experiments and complex physical models.

• Conduct research in uncertainty representation, quantification, and propagation; optimal design of experiments; inference for large-scale applications; and development of scalable mathematical and algorithmic approaches as well as software tools for the analysis and calibration of massive and streaming data sets resulting from scientific experiments and dynamical simulation.

• Applications include weather and climate science, power grid modeling, x-ray imaging and crystallography, quantum computing, and microbiology.

• Participate at all the stages of the implementation of the resulting methods from prototyping in high-level languages to the development in a high-performance computing environment.

• Be a part of a team that includes computational mathematicians, statisticians, computer scientists and domain scientists.

Position Requirements

• A completed Ph.D. in Mathematics, Statistics, or Computational Science.

• Expertise in computational mathematics; statistics; probability; uncertainty quantification and data assimilation (Bayesian analysis, deterministic and stochastic differential equations, Gaussian process modeling); and optimal design of experiments.

• Expertise in Software development in Python, and management of packages and teams developing advanced scientific software tools.

• Experience and knowledge in the following areas: scientific programming languages (C, C++, Fortran); linear algebra, real-valued and complex-valued calculus; combinatorial, stochastic, and robust optimization; parallel programming and high-performance computing; machine learning and reinforcement learning; and quantum computing and/or quantum networking.

• Demonstrated record of collaboration, team management, and supervision.

• Ability to interact with members of a multidisciplinary team.

• Develop, under supervision, high-quality scientific reports.

• Ability to model Argonne’s Core Values: impact, safety, respect, integrity, and teamwork.

Job Family

Research Development (RD)

Job Profile

Mathematics/Statistics 2

Worker Type

Regular

Time Type

Full time

As 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.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.