The Center for Nanoscale Materials (https://www.anl.gov/cnm) at Argonne National Laboratory (Chicago, US) has several immediate postdoctoral openings in the use of computational modeling and machine learning to interpret experimental x-ray, electron, and scanning probe characterization data. The postdoctoral researchers will work within the group of Dr Maria Chan (https://www.anl.gov/profile/maria-k-chan) on developing and applying algorithms and software for the use of first principles and atomistic modeling, together with machine learning, to accelerate the inversion of experimental characterization data. The postdoctoral researchers will be collaborating extensively with researchers at Argonne, other national laboratories, as well as universities.
Applicants should have expertise in some of the following:
- Density functional theory/molecular dynamics modeling of materials, e.g. high throughput calculations, structure prediction, classical and ab initio MD simulations.
- Simulation/analysis of experimental characterization signals, e.g. x-ray spectroscopy, electron microscopy, scanning tunneling microscopy, and neutron scattering.
- Use of machine learning approaches.
- Development and implementation of algorithms and software for materials modeling.
- High-performance and parallel computing.
Excellent communication and analytical skills are required. Ability to work independently and in an interdisciplinary collaborative environment, in close collaboration with experimentalists, is expected. Software programming experience is necessary. A PhD in computational physics, chemistry, materials science, chemical engineering, or a related field is required. Postdoctoral appointments are on a one-year basis, with a maximum term of three years, subject to performance evaluation. Reviews of applications will begin immediately until the position is filled.
Interested candidates should apply through the Argonne web site at http://www.anl.gov/careers (requisition number NST 408815). Please include a detailed curriculum vitae including a list of publications and the names and email addresses of three professional references.In your cover letter, please include:
- Description of previous relevant experience (modeling, experimental data simulation/analysis, machine learning, software development).
- Desired start date.
- US work authorization, if known (citizenship/visa type).
- Optional: If desired, please attach one representative publication that best showcases your work.
For additional questions, please contact Dr. Maria Chan (email@example.com).
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, 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 Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.