Über diese Assistant Physicist Stelle bei Argonne National Laboratory
The X-ray Imaging Group (https://www.aps.anl.gov/Imaging) of the X-ray Science Division (XSD) operates the full-field x-ray imaging beamlines at the Advanced Photon Source (APS) (https://www.aps.anl.gov/) and pursues collaborative research across materials, energy, environmental, geo-, and life sciences using micro- and nano-tomography and high-speed imaging.
With the APS Upgrade driving a substantial increase in data rates and experimental complexity, the group is investing in AI/ML-driven software, automation, and autonomous experimentation, including a planned fully autonomous, AI-driven tomography beamline — to keep pace with user demand and unlock new science. This work is carried out in close coordination with the APS Computational science and AI (CAI) (https://cai.xray.aps.anl.gov/) group and other APS AI efforts and activities spanning data, computing, and machine learning across the facility. The appointee will benefit from access to world-leading experimental and computational resources at Argonne, including the upgraded APS and the exascale Aurora supercomputer.
We seek highly motivated candidates with strong expertise in AI/ML and in the development of AI tools for scientific operations to join the X-ray Imaging Group. The successful candidate will lead the design and deployment of AI-powered software that supports user operations, automates data acquisition and analysis, and enables closed-loop, autonomous experiments across the group's beamlines.
Position Responsibilities:
Design, develop, and deploy AI/ML tools for x-ray imaging operations including reconstruction, super-resolution, spatiotemporal fusion, denoising, segmentation, and feature extraction — and integrate them into the beamline software stack.
Build closed-loop experimental workflows in which AI agents use streaming data and real-time reconstruction and analysis to steer measurement decisions, and contribute to the development of a fully autonomous, AI-driven tomography beamline as a flagship project for the group.
Collaborate with the APS Computation and AI (CAI) group and engage with other APS AI efforts and activities to align Imaging Group tools with facility-wide AI/ML infrastructure, data services, and computing resources, and to contribute to shared frameworks for autonomous experimentation.
Develop automated pipelines for acquisition, quality control, and downstream analysis that translate beamline-scientist expertise and currently manual operational steps into robust, reusable software.
Build and maintain pipelines for robust metadata capture and the systematic generation of curated, standardized datasets to support continual AI/ML model training and validation.
Provide on-site support for user operations and data collection across the X-ray Imaging Group beamlines, working directly with beamline staff and users during experiments.
Contribute to the longer-term extension of AI-enabled automation and autonomy across Imaging Group modalities, including micro- and nano-tomography and high-speed imaging.
Prepare experiments and instruments for remote and AI-driven operation.
Present research results through publications, conferences, and scientific meetings.
May be required to perform other duties as assigned.
Position Requirements
Ph.D. in computer science, electrical engineering, computational physics, computational materials science, applied mathematics, or a closely related field.
Demonstrated expertise in AI/ML applied to imaging or scientific data, including hands-on experience developing and deploying deep-learning models (e.g., CNNs, vision transformers, diffusion models, or related architectures).
Strong scientific software development skills in Python and modern deep-learning frameworks (e.g., PyTorch, TensorFlow), including experience with distributed training on high-performance computing resources.
Experience with high-performance computing (HPC) and/or cloud environments.
Experience with version control (e.g., Git) and collaborative software development practices.
Experience working with experimental imaging data, ideally at a synchrotron, electron microscopy, medical imaging, or comparable facility.
Ability to work effectively both independently and in a collaborative, team-based research environment.
Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.
Interpersonal skills, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory.
Preferred Knowledge, Skills, and Experience
Experience developing AI/ML methods specifically for x-ray imaging, tomography, or high-speed imaging applications.
Experience designing or contributing to automated, remote, or closed-loop ("self-driving") experimental workflows, including real-time data reduction, on-the-fly reconstruction, and AI-based experimental steering.
Experience collaborating with facility-level computing, data, or AI groups to deploy software into production scientific environments.
Experience handling high-rate, large-volume imaging datasets and developing high-throughput reconstruction or analysis pipelines.
Experience contributing to open-source scientific software projects.
Familiarity with metadata standards, data management, and curation practices that support reproducible science and ML training datasets.
Familiarity with beamline data acquisition systems, detectors, or controls software, sufficient to integrate AI tools into operational workflows.
Job Family
Research Development (RD)Job Profile
Physics 2Worker Type
RegularTime Type
Full timeThe expected hiring range for this position is $94,486.00 - $147,398.94.Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
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