Postdoctoral Appointee – Physics-Aware Multimodal Deep Learning Argonne National Laboratory

Lightsources.org; Aurora (IL); On-site; USD 70,000 - 111,000; Full time

Job summary

An established industry player is seeking a postdoctoral appointee to develop innovative physics-aware deep learning methods. This role involves creating models that enhance scientific data analysis and instrument tuning, utilizing cutting-edge computational resources. The successful candidate will join a dynamic, interdisciplinary team, contributing to high-impact research and publications. This position offers an exciting opportunity to work at the forefront of scientific discovery, leveraging deep learning to solve complex challenges in the physical sciences. If you are passionate about applying your expertise in a collaborative environment, this role is perfect for you.

Qualifications

• PhD in a relevant field completed in the past 5 years or soon to be completed.
• Experience with deep learning libraries and physics-informed techniques.

Responsibilities

• Develop physics-aware deep learning methods for scientific data analysis.
• Publish results in high impact journals and present at conferences.

Skills

Deep Learning

Physics-informed Neural Networks

Programming (Python, C/C++, Go, Rust)

Signal Processing

Uncertainty Quantification

Multi-modal Deep Learning

Communication Skills

Education

PhD in relevant field

Tools

TensorFlow

PyTorch

JAX

Git

Job description

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:

• PhD in a relevant field completed in the past 5 years or soon to be completed.
• 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 ODEs, or other physics-aware DL techniques.
• Skill in programming languages such as Python, C/C++, Go, Rust etc.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

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 different backgrounds and skill sets.

Job Family: Postdoctoral

Job Profile:Postdoctoral Appointee

Worker Type:Long-Term (Fixed Term)

Time Type:Full time

The expected hiring range for this position is $70,758.00 – $110,379.55.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any 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.

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