Postdoctoral Appointee: Mathematics and optimization of Digital Twins
Argonne National Laboratory
Lemont, USA
Job posting number: #7279925 (Ref:418939)
Posted: September 12, 2024
Job Description
The Mathematics and Computer Science Division, MCS, at Argonne National Laboratory seeks candidates for a postdoctoral position in computational mathematics and optimization of digital twins. In this role you will perform research in analysis and implementation of algorithms for solving optimization problems that involve digital twins to model complex systems. Applications include, but are not limited to, data assimilation and control for self-assembly systems, optimal experimental design, and control for autonomous devices.
Research will be performed in close collaboration with leading scientists in applied mathematics and computer science. Researchers will have access to cutting-edge extreme-scale computing facilities. Argonne provides a collaborative, open-science environment where scientists from various disciplines and backgrounds are engaged in solving challenging fundamental and practical problems. We actively promote diversity in backgrounds, experiences, and perspectives.
For more information on the applied mathematics, numerical software, and statistics group at Argonne, see https://www.anl.gov/mcs/lans
Position Requirements
- Recent or soon-to-be completed PhD (typically within the last 0-5 years) in applied mathematics, statistics, computer science, industrial engineering, machine-learning, or related field
- Experience in nonlinear optimization, PDE-constrained optimization, machine learning, or optimal control
- Interest in contributing to numerical software development
- Experience with (or willingness to explore) high-performance computing environments
- Ability to model Argonne's core values of impact, respect, integrity, safety and teamwork
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.
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.
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.
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