Postdoctoral Appointee - NST Computational Modeling and Machine Learning (argonne)
Job posting number: #75032 (Ref:416937)
This Job Posting is Expired.
Job Description
The Center for Nanoscale Materials (https://www.anl.gov/cnm) at Argonne National Laboratory has an immediate opening for a Postdoctoral Appointee in the use of computational modeling and machine learning to interpret experimental x-ray, electron, and scanning probe characterization data, especially for energy materials. 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, especially x-ray spectroscopies.
Position Requirements
- A PhD completed within the last 3 years or soon to be completed; in Physics or a related field
- Density functional theory modeling of materials, e.g. high throughput calculations, structure prediction, classical and ab initio MD simulations
- Simulation of experimental characterization signals, e.g. x-ray spectroscopy, electron microscopy, scanning tunneling microscopy, and neutron scattering
- Use of machine learning approaches JOB IS FROM: postdocjobs.oneVIEW
- Development and implementation of algorithms and software for materials modeling
- High-performance and parallel computing
- Excellent communication and analytical skills
- Ability to work independently and in an interdisciplinary collaborative environment, in close collaboration with experimentalists.
- Ability to model Argonne’s core values of impact, safety, respect, integrity, 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.
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