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:
- Brookhaven Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $78,000 - $100,000/ year. You will be placed at the level and salary commensurate with your experience. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group.
- BNL policy requires that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events.
