Postdoctoral Research Associate - MSD - Van De Waals Materials
Job posting number: #7175655 (Ref:416464)
Posted: September 2, 2023
Application Deadline: Open Until Filled
Postdoctoral Appointee positions are available within the Materials Science Division (MSD) at Argonne National Laboratory. A successful candidate will work with a group of scientists in MSD to explore the evolution of van der Waals materials under external electric and optical stimuli using coherent and / or ultrafast X-ray photons. The candidate will be able to take full advantage of the highly coherent X-rays from the upgraded Advanced Photon Source (APS), the THz laser within MSD and the aberration-corrected Lorentz transmission electron microscope within MSD, as well as X-ray Free Electron Laser facilities around the world.
In this role the successful candidate will:
Perform pioneering experiments to reveal the driven spatiotemporal evolution of electronic order and domains in van der Waals materials;
Develop novel experimental methods and simulations for understanding and interpreting the experimental results;
Publish results in refereed journals and make oral presentations at meetings, conferences, symposia, and seminars.
We expect the successful candidate to have:
A Ph.D. in physics, applied physics, materials science or a related field.
Knowledge in one of the following areas: van der Waals materials, ferroelectrics, correlated oxides or energy conversion and storage materials.
Experience in one of the following: X-ray diffraction, crystallography, Lorentz transmission electron microscopy, ultrafast studies of materials
Experience with advanced data analysis implemented in languages such as Python or MATLAB.
Strong written and oral communication skills.
Ability to collaborate and work with a team in an interdisciplinary environment.
- Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.
Having one of following experiences would be highly desirable:
Fabrication and characterization of quantum materials and heterostructures
Experience with machine learning/deep learning methods
To be eligible, candidates must have completed their Ph.D. within the last three years, or be expecting their Ph.D. within the next 12 months.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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