Postdoctoral Research Associate

Requisition Id 15490

­­Overview:
The Multimodal Sensor Analytics group in the Electrification and Energy Infrastructure Division (EEID) is seeking a postdoctoral researcher with proven expertise in computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and industrial imaging data. You will directly contribute to developing and deploying algorithms for multi-modal tomography (X-ray, neutron, and electron), advancing methods for non-destructive evaluation (NDE) and scientific imaging. This position emphasizes bridging cutting-edge ML/AI with real-world imaging systems in collaboration with experimental scientists, leveraging facilities such as the Manufacturing Demonstration Facility (MDF). This role offers a unique opportunity to drive impactful research on sparse scientific imaging while building a strong research profile in computational imaging and ML for CT.

Major Duties/Responsibilities:

• Lead research on sparse-view and limited-angle CT algorithms for scientific and industrial applications.
• Develop and apply ML/AI-driven computational imaging methods (e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection.
• Advance algorithms for multi-modal tomography (X-ray, neutron, electron).
• Collaborate with experimentalists to validate methods on real-world systems.
• Publish in high-impact journals and present at leading national and international conferences.
• Ensure compliance with environment, safety, health, and quality program requirements.
• Uphold strong values and ethics in collaborative research.
• Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Basic Qualifications:

• Ph.D. in electrical engineering, computer science, or related discipline completed within the last five years.
• Demonstrated expertise in computed tomography (CT), with experience in sparse-view and/or limited-angle reconstruction and published research in this area.
• Experience applying ML/AI algorithms to scientific imaging data (e.g., CT, microscopy, multi-modal datasets).
• A strong record of productive and creative research, demonstrated by peer-reviewed publications and conference presentations.

Preferred Qualifications:

• Hands-on experience with ML/AI frameworks (PyTorch, TensorFlow) for tomographic reconstruction and analysis.
• Familiarity with physics-informed, plug-and-play (PnP), or generative models for imaging.
• Prior experience with multi-modal tomography (X-ray, neutron, electron).
• Knowledge of high-performance computing or cloud environments for large-scale data.
• Strong collaboration skills and ability to work in interdisciplinary teams.

Special Requirements:
Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to with the position title and number referenced in the subject line.

Instructions to upload documents to your candidate profile:

• Login to your