Postdoctoral Appointee - NST - Machine Learning for Electronic Structure and Materials Modeling

The Midwest Integrated Center for Computational Materials (http://miccom-center.org/ ) at Argonne National Laboratory and the University of Chicago has an immediate postdoctoral opening in the use of machine learning in electronic structure calculations and advanced sampling. The postdoctoral researcher will work within the groups of Professor Giulia Galli (https://galligroup.uchicago.edu/ ) and Dr Maria Chan (https://www.anl.gov/profile/maria-k-chan ) on developing and applying algorithms and software for the use of machine learning techniques to accelerate the computation of electronic structure properties and experimentally-informed advanced sampling.

Questions should be directed to [email protected] . Please note that only applications through the Argonne website will be considered.

Postdoctoral appointments are on a one-year basis, with a maximum term of three years, subject to performance evaluation.

MICCoM is a US Department of Energy funded computational materials science software center, involving highly collaborative research to advance computational prediction of materials properties. Argonne National Laboratory offers excellent compensation and benefit packages, and postdoctoral researchers receive extensive career development guidance.

Position Requirements

• A PhD in Computational Physics, Chemistry, Materials Science, Chemical Engineering, or a related field. Degree must have been received within the last 3 years or upcoming year.
• Solid communication and analytical skills
• Ability to work independently and in an interdisciplinary collaborative environment is expected
• Software programming experience is necessary
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Applicants should have expertise in some of the following:

• Density functional theory modeling of materials, especially in the areas of electronic excitations

• Use of artificial intelligence/machine learning (AI/ML) approaches for computational materials problems

• Simulation of experimental measurements such as scattering and spectroscopy

• Structure search or sampling approaches

• Development and implementation of algorithms and software for materials modeling

• High-performance and parallel computing

Interested candidates should include with their Workday application:

1. A detailed curriculum vitae including a list of publications and the names and email addresses of three professional references

2. A cover letter, including

• description of previous materials modeling experience (techniques, materials, key contributions)

• description of AI/ML experience

• description of software development experience

3. Expected start date

4. USA citizenship/visa/work eligibility status (if known)

5. One representative publication that best showcases your work

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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