The Surface Scattering and Microdiffraction (SSM) group in X-ray Science Division (SSM-XSD) of Advanced Photon Source (APS) at Argonne National Laboratory leads pioneering efforts in developing cutting edge X-ray instrumentation and techniques, for enabling in-situ experiments with x-ray diffraction microscopy investigation in areas of physics, chemistry, and material sciences. 

The coming APS upgrade (APS-U) will enable X-ray diffraction microscopy to image in-situ structural information with a 150 nm focused beam. To accomplish this, we are currently seeking to fill a Postdoctoral Appointee position for a 2+(1)-year period to support development of data analysis and computational algorithms for X-ray diffraction microscopy experiments at instrument 34-ID-E and electron microscopy studies at Argonne’s Center for Nanoscale Materials. 

The successful candidate will participate in novel in-situ experiments with precise control of sample nano-indentation and/or corrosion. The candidate will work in a multidisciplinary team, including physicists, chemists, and engineers, and will lead the development of data analysis pipelines in support of innovative in-situ experiments.  These developments will include software/algorithms (including machine learning) for multimodal data analysis, with a goal of providing real-time feedback of effects seen in the sample during the on-going experiment.

Position Requirements

Required Knowledge, Skills and Experience:

• Ph.D. degree in physics, chemistry, materials science, engineering, or a related discipline obtained within the last three years.
• Programming expertise in C/C++, Python, Matlab, or other scientific programming languages;
• Considerable experience in experimental data analysis including creativity in building new computational algorithms.
• Demonstrated strong analytical and problem-solving skills.
• Excellent oral and written communication skills.
• Good collaboration skills and be able to work with a team.

Preferred Knowledge, Skills and Experience:

• Experience applying machine learning to scientific data analysis.
• Experience / interest in developing materials simulations, including molecular dynamics.
• Experience / interest in developing sample environments for X-ray microscopy at synchrotron beamlines.
• Experience in materials studies of structural materials or corrosion.
• The ability to conduct research such as designing experiments (real or simulated) and addressing fundamental and / or applied scientific questions.
• SEM and TEM experience are welcome but not required.
• Experience with automation, control, and instrument infrastructure

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