Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience with modern architectures (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 evaluation metrics relevant to imaging AI (Dice, IoU, ROC/AUC, sensitivity/specificity)
Experience with version control and collaborative development (e.g., Git)
Demonstrated ability to independently lead a research project through execution and publication
Demonstrated interest or experience in mentoring junior researchers (postdocs, PhD students, or equivalent)
Skills
Python
PyTorch
TensorFlow
Git
DICOM
About the role
Dr. Pritam Mukherjee’s lab is seeking a highly motivated and experienced Senior Scientist to lead the development of advanced machine learning (ML), deep learning (DL), and foundational AI models for medical imaging, with a primary focus on radiology and a broader interest in biomedical informatics. This role is well-suited to someone who thinks like a researcher first: someone who tracks the frontier of AI research (including large language models and multimodal foundation models) and translates emerging methods into novel approaches for imaging problems such as segmentation, quantification, detection, and image-based biomarker discovery across CT, MRI, and X-ray.
This position sits within a well-resourced, data-rich research environment, with access to large, multi-institutional datasets and St. Jude Children’s Hospital’s excellent high-performance computing resources. Some datasets will already be curated, but others will require the lab to perform its own data curation and annotation, so experience with these tasks is a valuable asset. This role is expected to focus on high-impact model development, novel methods research, and academic output, with opportunities for clinical translation where appropriate.
This role offers a unique combination of independence and mentorship: the successful candidate will have the opportunity to independently lead research projects, publish in strong venues, and help mentor postdoctoral fellows and PhD students in the lab.
Job Responsibilities:
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Independently conceive, lead, and execute research projects applying state-of-the-art ML/DL and foundation models to medical imaging problems (segmentation, quantification, detection, and related tasks) across CT, MRI, and X-ray
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Design and implement 2D and 3D model architectures (CNNs, transformer-based, and foundational/vision-language models)
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Apply and adapt large language models (LLMs) to biomedical and imaging-adjacent problems, including training, fine-tuning, and inference/deployment of LLMs
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Stay current with the broader AI research literature (imaging, LLMs, multimodal foundation models) and proactively bring new methods into the lab’s research program
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Build scalable pipelines for data preprocessing, model training, evaluation, and deployment
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Develop quantitative imaging methods (e.g., volumetrics, density measurements, biomarker extraction)
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Leverage curated, multi-institutional datasets to ensure model generalizability and robustness
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Collaborate closely with radiologists, clinicians, and engineering teams to ground research in what is actually useful and feasible in clinical practice
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Mentor and provide technical guidance to postdoctoral fellows and PhD students, including project design, code/method review, and manuscript preparation
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Ensure reproducibility and traceability of experiments (data, model, and code versioning)
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Utilize modern AI-assisted development tools (e.g., LLM-based coding agents) to accelerate development and improve code quality
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Participate in and help establish team-based development practices (code reviews, Git, testing frameworks)
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Lead and contribute to manuscripts, grant applications, and technical reporting
Minimum Education and/or Training:
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Bachelor’s degree in relevant scientific area is required
Minimum Experience:
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Ten (10) years relevant work experience is required
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Eight (8) years of relevant work experience is required with a Master’s degree
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Five (5) years of relevant work experience is required with a PhD
Preferred Qualifications
Candidates are not expected to have all of the following, but experience with one or more is a strong plus:
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Experience training, fine-tuning, and/or deploying LLMs (inference optimization, prompt/agent design, or related work)
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Experience with imaging modalities beyond radiology (e.g., microscopy, pathology, or other biomedical imaging)
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Experience working directly with clinicians, with some clinical knowledge or exposure that helps distinguish which research directions and model outputs are actually useful in practice
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Familiarity with DICOM and medical imaging workflows
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Broader background in biomedical informatics (e.g., multi-omics, EHR data, clinical NLP)
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Experience with large, multi-institutional datasets, including data curation and annotation
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Experience with cloud or high-performance computing environments
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Experience deploying models into research environments
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Exposure to translational AI development or regulatory-aware research (e.g., FDA pathways), though this is not the focus of the role
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PhD in Computer Science, Data Science, Biomedical Engineering, or a related field (or equivalent research experience)
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Postdoctoral research experience preferred
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Strong record of independent research and publication in ML/DL applied to imaging and/or biomedical data
Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
•
Experience with modern architectures (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 evaluation metrics relevant to imaging AI (Dice, IoU, ROC/AUC, sensitivity/specificity)
•
Experience with version control and collaborative development (e.g., Git)
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Demonstrated ability to independently lead a research project through execution and publication (ability to also help conceive of new project directions is a plus, but not required)
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Demonstrated interest or experience in mentoring junior researchers (postdocs, PhD students, or equivalent)