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 multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. 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 for such models include high-resolution 3D imaging, time-resolved materials characterization, and atomic structure determination. Scientific instrument data is often multimodal in nature and developing DL models that can process and learn from multiple data streams in real-time is key to unlocking the full potential of such instruments. The postdoctoral appointee will be responsible for developing such methods that are broadly applicable across the physical sciences but applied initially to x-ray characterization needs. They will publish results in high impact journals, present at conferences and 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 some of the world’s largest supercomputers (Polaris, Aurora) and one of the brightest synchrotron x-ray sources in the world (APS). Candidates with a background in deep learning, computational physics, computational materials science, inverse problems, signal processing, x-ray science etc. are encouraged to apply. **Position Requirements** **Required Knowledge, Skills and Experience** **:** + Ph.D. in a related field obtained within the last three years. + Knowledge of x-ray/optical/electron 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 ordinary differential equations, or other physics-aware DL techniques. + Skill in programming languages such as Python, C/C++, Go, Rust etc. **Preferred Knowledge, Skills and Experience** **:** + Experience with version control such as Git and collaborative software development. + Experience with uncertainty quantification and multi-modal deep learning. + Experience with distributed training. + 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 _As 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._ _Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department._ _All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment._