Postdoctoral Research Associate - AI for Medical Imaging & Biomedical Informatics

St. Jude Children's Research Hospital • Memphis, TN

1d

About The Position

Dr. Pritam Mukherjee's lab is seeking a highly motivated postdoctoral researcher to develop advanced machine learning, deep learning, and foundational AI models for medical imaging, primarily radiology, with broader work in biomedical informatics. The role involves tracking current AI research, including large language models and multimodal foundation models, and applying emerging methods to imaging problems such as segmentation, quantification, detection, and image-based biomarker discovery across CT, MRI, and X-ray. Applicants may have a PhD, MD, or MD/PhD background.

The position is based in a well-resourced, data-rich research environment, with access to large multi-institutional datasets and St. Jude Children's Hospital's high-performance computing resources. Some datasets are curated, while others require lab-led data curation and annotation. This is a 2–3 year appointment with a flexible start date. The lab supports career development, including pursuing independent funding such as a K99/R00 or similar award. Optional mentorship opportunities with junior lab members are also available.

Requirements

• PhD or MD (or MD/PhD) in Computer Science, Data Science, Biomedical Engineering, Medicine, or a related field.
• Demonstrated experience with segmentation, detection, and/or quantitative imaging algorithms in 2D and/or 3D.
• Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with modern architectures, such as U-Net variants, detection frameworks, transformers, or foundational models.
• Demonstrated, up-to-date knowledge of the AI research landscape, including recent developments in foundation models and/or LLMs.
• Strong understanding of imaging AI evaluation metrics, including Dice, IoU, ROC/AUC, and sensitivity/specificity.
• Experience with version control and collaborative development, such as Git.
• Intellectually curious, research-driven, and genuinely engaged with current AI literature.

Nice To Haves

• Experience training, fine-tuning, and/or deploying LLMs, including inference optimization, prompt or agent design, or related work.
• Experience with imaging modalities beyond radiology, such as microscopy, pathology, or other biomedical imaging.
• Experience or interest in working directly with clinicians, with clinical knowledge or exposure that helps assess practical usefulness of research directions and model outputs.
• Familiarity with DICOM and medical imaging workflows.
• Broader biomedical informatics background, such as multi-omics, EHR data, or clinical NLP.
• Experience with large multi-institutional datasets, including data curation and annotation.
• Experience with cloud or high-performance computing environments.
• Experience deploying models into research environments.
• Exposure to translational AI development or regulatory-aware research, such as FDA pathways.

Responsibilities

• Design and implement 2D and 3D model architectures, including CNNs, transformer-based models, and foundational or vision-language models, for medical imaging problems.
• Apply and adapt large language models to biomedical and imaging-adjacent problems, including training, fine-tuning, inference, and deployment.
• Stay current with AI research in imaging, LLMs, and multimodal foundation models, and bring new methods into ongoing research projects.
• Build scalable pipelines for data preprocessing, model training, evaluation, and deployment.
• Develop quantitative imaging methods, such as volumetrics, density measurements, and biomarker extraction.
• Use curated multi-institutional datasets and contribute to data curation and annotation when needed.
• Collaborate with radiologists, clinicians, and engineering teams to ground research in clinical usefulness and feasibility.
• Ensure reproducibility and traceability through data, model, and code versioning.
• Use modern AI-assisted development tools, such as LLM-based coding agents, to accelerate development and improve code quality.
• Participate in team-based development practices, including code reviews, Git, and testing frameworks.
• Contribute to manuscripts, grant applications, including independent funding opportunities if desired, and technical reporting.
• Optionally support junior lab members, such as PhD students and research assistants, through mentorship.

Benefits

• Significant opportunities for first-authorship on high-impact manuscripts.
• Active participation in multi-institutional research collaborations.
• Dedicated support for pursuing independent funding, such as K99/R00 or similar career development awards, for those interested.
• Optional mentorship opportunities for those interested in building mentoring experience.
• Opportunity to build a strong academic portfolio, with the option to pursue translational or clinical impact where projects warrant it.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree