As one of the newest, most advanced synchrotron light sources in the world, the National Synchrotron Light Source II (NSLS-II) enables its growing research community to study materials with nanoscale resolution and exquisite sensitivity by providing cutting-edge capabilities. This facility is open to users from academia and industry. Its operations coincide with a moment when the world is entering a new era with a global economy driven predominantly by scientific discoveries and technological innovations.
Position Description
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.
Essential Duties and Responsibilities:
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.
Required Knowledge, Skills, and Abilities:
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.
Preferred Knowledge, Skills, and Abilities:
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.
Additional Information: