Postdoctoral Appointee: Physics-aware Deep Learning - Military Veterans

The Advanced Photon Source (APS) ( https://www.aps.anl.gov/ ) at Argonne National Laboratory (Lemont, Illinois, US (near Chicago)) invites applicants for a postdoctoral position to develop physics-aware deep learning (DL) methods. At the APS, we are developing physics-aware DL models for scientific data analysis, autonomous experiments, instrument tuning etc. By incorporating prior physics knowledge into DL model design and training, these models outperform traditional methods even without labeled training data ( https://www.nature.com/articles/s41524-022-00803-w ). Application spaces include high-resolution 3D imaging, time-resolved materials characterization and atomic structure determination.

The postdoctoral appointee will be responsible for developing such methods for various x-ray characterization techniques, with the focus area determined by a combination of the candidate's background and the APS's needs. They will publish results in high impact journals, present at conferences and also work with the software engineering team to translate the models into production.

The successful candidate will be part of a cross-lab, highly inter-disciplinary team of experts in ML, applied math, HPC, signal processing, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including the world's first exascale computer (Aurora) and one of the brightest synchrotron x-ray sources in the world (APSU).

Candidates with a background in computational physics, computer science, electrical engineering, computational materials science, inverse problems, signal processing, x-ray science etc. are encouraged to apply.

Position Requirements

Qualifications:

• Must have obtained a PhD within the last three years in a related field.
• Knowledge of x-ray/electron/optical physics, including diffraction, optics, detectors, scattering etc.
• Experience with deep learning (DL) libraries such as Tensorflow, PyTorch, JAX etc.
• Experience with physics-informed neural networks, automatic differentiation, neural ODEs or other physics-aware DL techniques.

Preferred:

• Experience with version control such as Git and collaborative software development.
• Skill in programming languages such as Python, C/C++, Go, Rust etc.
• Experience in applying DL to X-ray, electron or optical characterization data.
• Skill in written and oral communications.
• Experience interacting with scientific staff and research groups. Ability to work effectively as a member of a team. Ability to effectively communicate with people of diverse backgrounds and skill sets.

Job Family
Postdoctoral Family

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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