Brookhaven Science Associates-posted 2 months ago
$78,000 - $100,000/Yr
Full-time • Entry Level
Upton, NY
1,001-5,000 employees
Professional, Scientific, and Technical Services

The Coherent Hard X-ray (CHX, 11-ID) beamline at NSLS-II seeks a postdoctoral researcher with a strong background in computational imaging, data science, or scientific computing. This position will focus on developing advanced image reconstruction methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based methods. As part of a DOE-BER-funded effort to develop a quantum-enhanced X-ray microscope for low-dose biological imaging, the successful candidate will work closely with experimental physicists, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning.

  • Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities.
  • Maintain and extend Python-based software packages for data processing and simulation.
  • Analyze high-throughput photon event data to extract spatial and temporal correlations.
  • Collaborate with experimental staff on algorithm validation and feedback-driven experiment design.
  • Optimize pipelines for performance, parallelization, and near real-time operation during beam time.
  • Contribute to simulation tools to test imaging concepts, predict performance, and support proposal development.
  • Ph.D. in Physics, Computer Science, Applied Mathematics, Engineering, or a related field.
  • Strong programming experience.
  • Knowledge of inverse problems, image reconstruction, or signal processing.
  • Experience with algorithm development for noisy, sparse, or large-scale datasets.
  • Demonstrated ability to work collaboratively with experimentalists and adapt code for real-world data.
  • Familiarity with compressed sensing and/or convex optimization (e.g., total variation minimization).
  • Expertise in Python, including use of scientific libraries (e.g., NumPy, SciPy, scikit-image, PyTorch/TensorFlow).
  • Experience with deep learning or machine learning approaches to image denoising and reconstruction.
  • Prior exposure to experimental data from photon-counting or time-resolved detectors.
  • Experience with Bayesian methods, uncertainty quantification, or real-time data processing.
  • Familiarity with distributed computing or HPC environments.
  • Comprehensive employee benefits program.
  • Salary range of $78,000 - $100,000/year, commensurate with experience.
© 2024 Teal Labs, Inc
Privacy PolicyTerms of Service