• High-energy physics (HEP) detectors
• Neuromorphic computing
• FPGA/ASIC design
• Machine learning for edge processing

The successful candidate will work with a multi-institutional, multidisciplinary team to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments.

This position resides in the Relativistic Nuclear and High Energy Physics group, Physics Division, Physical Science Directorate, at ORNL.

Major Duties / Responsibilities

Neuromorphic & Machine Learning Algorithm Development:

• Develop and train Spiking Neural Networks (SNNs) for clustering, classification, and interpolation of pixel detector data
• Explore spike-based data encoding/decoding strategies
• Perform hyperparameter optimization using HPC resources

Detector Simulation & Data Processing:

• Use and extend Allpix2 and TCAD-based simulation tools
• Generate large simulation datasets for algorithm training and validation
• Integrate simulation with neuromorphic platforms

FPGA / Hardware-Embedded AI:

• Deploy SNNs to FPGA-based neuromorphic hardware (NeuroSpike/NeuroSpark)
• Generate RTL using HLS workflows; evaluate resource usage, latency, and performance
• Participate in hardware test benches for real detector systems

Experimental Detector Work:

• Test and characterize pixel detectors (CMS HL-LHC, Timepix4, AC-LGAD, PDCs)
• Demonstrate on-sensor data processing with hardware prototypes
• Potential involvement in beam tests or radiation-source laboratory setups

Basic Qualifications

• Ph.D. in Physics, Electrical Engineering, Computer Science, or a closely related field
• Experience in at least one of the following areas:
  • FPGA programming (VHDL/Verilog, HLS)
  • Pixel detectors in high-energy physics or radiation detection
  • Neuromorphic computing or Spiking Neural Networks
  • Machine learning, AI/ML algorithms, or scientific computing
• Strong programming skills in Python and/or C++

Preferred Qualifications

• Ability to work collaboratively in a multidisciplinary team
• Strong communication skills for clear documentation and presentation of research
• Experience with:
  • Allpix2, GEANT4, detector simulation, or TCAD
  • FPGA toolchains (Xilinx Vitis/Vivado, Intel Quartus, HLS tools)
  • HPC environments or GPU-accelerated computing
  • On-detector firmware or data acquisition systems
• Familiarity with HEP data formats and reconstruction
• Background in semiconductor sensor physics or microelectronics

Appointment Information

• Applicants cannot have received their Ph.D. more than five years prior to the application date
• Must complete all degree requirements before starting
• Appointment length: up to 24 months with potential extension, contingent on performance and funding

Letters of Recommendation

• Three letters required
• Upload directly to the application or email postdocrecruitment@ornl.gov with the position title and number referenced

Security, Credentialing, and Eligibility Requirements

• Position requires ability to obtain and maintain HSPD-12 PIV badge
• Real ID-compliant identification required for employment
• Must complete and pass a Federal Tier 1 background check
• Foreign nationals: candidates without 3 consecutive years U.S. residency require Local Site Specific Only (LSSO) risk determination

About ORNL

• U.S. Department of Energy (DOE) Office of Science national laboratory
• 80-year legacy addressing national scientific challenges
• Team of 7,000+ employees
• Environment valuing diverse perspectives and backgrounds
• Competitive pay and benefits, including:
  • Medical, dental, and vision plans
  • 401(k) and contributory pension plans
  • Life and disability insurance
  • Generous vacation, holidays, and parental leave
  • Legal insurance, identity theft protection, and educational assistance
  • On-site amenities: fitness, banking, cafeteria
  • Relocation assistance and employee discounts

Application Instructions

• Position open for a minimum of 5 days; closes when qualified candidate is identified
• File types accepted: Word (.doc/.docx), Adobe PDF, RTF, HTML (.htm/.html), up to 5MB
• Third-party resumes not accepted

Accommodation: For difficulty using the online application system or disability-related accommodations, email ORNLRecruiting@ornl.gov

Equal Employment Opportunity

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.