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Argonne National Laboratory Postdoctoral Appointee - Deep learning at the edge in Lemont, Illinois

Postdoctoral Appointee - Deep learning at the edge

Requisition Number: 409191 Location: Lemont, IL

Functional Area: Research and Development Division: XSD-X-Ray Science Division

Employment Category: Temporary 6 Months or Greater Education Required: Not Indicated

Level (Grade): 700 Shift: 8:30 - 5:00 Share: Facebook LinkedIn Twitter

The Advanced Photon Source (APS) ( at Argonne National Laboratory (Chicago, US) invites applicants for a postdoctoral position to develop DL-accelerated data compression and analysis on the edge through ASICs.

At the APS, we are developing next-generation pixel array detectors for X-ray science in particular for high-throughput coherent X-ray imaging. Coherent imaging methods (including Ptychography) provide a powerful means of imaging materials at resolution beyond the limits of x-ray optics and under operando conditions but require challenging data handling and computational resources.

The successful candidate will lead the development of neural network based ASIC implementations of data compression and analysis methods at the edge.The successful candidate will be part of a cross-lab, highly inter-disciplinary team of experts in machine learning, ASIC design, advanced optimization, data compression, coherent imaging and X-ray detector development. 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 electrical engineering, computer engineering, machine learning, computational physics, image processing, inverse problems, x-ray science etc. are encouraged to apply.

We are seeking a combination of the following knowledge, skills and experience:

  • Experience with mixed-signal ASIC designs.
  • Knowledge of digital logic designs using Verilog and/or System Verilog.
  • Knowledge of VLSI mixed-signal ASIC design tools (CAD/EDA) and methodologies.
  • Skill in Python programming.
  • Experience with deep learning libraries (Keras, Tensorflow, PyTorch etc.).
  • Knowledge of front-end and back-end ASIC design elements, such as layout, physical verification, and design verification.
  • Experience with building analog or digital test-benches and carry-out mixed analog and digital circuit simulations.
  • Experience with Git/GitHub.
  • Experience implementing deep learning models on the edge.
  • Experience with digital encoding methods for compression (e.g., run-length) and serial links (e.g., Aurora).
  • Advanced digital verification techniques using System Verilog and UVM methodologies.
  • Experience with FPGA designs.
  • Experience with analog ASIC designs.
  • 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.

Preferred Qualifications:

  • Experience with synchrotron/XFEL experiments.
  • Experience with experimental automation.
  • Knowledge of readout integrated circuits for radiation sensors, including signal processing, signal filtering, signal conversion, data transmission, high speed electronics.

Questions about the posting should be directed to or

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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.