Urgent! Postdoctoral Research Associate – Computational Imaging and Data Science Job Opening In Upton – Now Hiring Brookhaven National Laboratory
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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 National Laboratory requires all non-badged personnel including visitors to produce a REAL-ID or REAL-ID compliant documentation to access Brookhaven National Laboratory – view more information atwww.bnl.gov/real-id.
This is due to nationwide identification requirements for federal site access as required by the federalREAL ID Act.
Those not in possession of a REAL ID-compliant document will not be permitted to access the site which includes access to the Laboratory for interviews.
About Us
Brookhaven National Laboratory (www.bnl.gov) delivers discovery science and transformative technology to power and secure the nation’s future.
Brookhaven Lab is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and more than 70 years of pioneering research.
The Lab is primarily supported by the U.S. Department of Energy’s (DOE) Office of Science.
Brookhaven Science Associates (BSA) operates and manages the Laboratory for DOE.
BSA is a partnership between Battelle and The Research Foundation for the State University of New York on behalf of Stony Brook University.
BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL | Opportunities for Veterans at Brookhaven National Laboratory.
Equal Opportunity/Affirmative Action Employer
Brookhaven Science Associates is an equal opportunity employer that values inclusion and diversity at our Lab.
We are committed to ensuring that all qualified applicants receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a veteran, disability or any other federal, state or local protected class.
BSA takes affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities.
We ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.
Please contact us to request accommodation.
*VEVRAA Federal Contractor
BSA employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1A.
You will be asked to disclose any such participation at the time of hire for review by Brookhaven.
The full text of the Order may be found at:
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Unlock Your Postdoctoral Research Potential: Insight & Career Growth Guide
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Great news! Brookhaven National Laboratory is currently hiring and seeking a Postdoctoral Research Associate – Computational Imaging and Data Science to join their team. Feel free to download the job details.
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