Research Associate - FELIX Lab

Position Summary:

The Felix Lab is seeking a highly motivated and detail-oriented Research Associate to join our team focused on the early detection of pancreatic neoplasms using artificial intelligence and advanced imaging analysis. The candidate will lead data management, contribute to ongoing multi-institutional collaborations, and play a key role in the development of AI-driven diagnostic tools that improve early detection and characterization of pancreatic lesions on CT imaging.

• Oversee clinical data collection, de-identification, and organization of a large patient imaging cohort using REDCap and secure data systems.
• Perform manual image segmentation and curate CT imaging datasets for radiomics analysis.
• Collaborate with AI/ML engineers, postdocs, and radiologists to train, evaluate, and refine models for tumor detection and classification.
• Coordinate cross-departmental efforts, including pathology correlations and multidisciplinary team meetings.
• Lead and co-author research manuscripts, abstracts, and grant applications.
• Mentor postdoctoral fellows and lead onboarding and training efforts for new team members.

• MD or PhD in Radiology, Medical Imaging, Biomedical Engineering, Data Science, or related field.
• Demonstrated experience in clinical research and medical imaging (CT preferred).
• Strong data management skills and experience with REDCap, Excel, and SQL.
• Background in radiomics, machine learning, or medical AI is highly desirable.
• Excellent communication and leadership skills, with experience guiding junior researchers.

• Experience in abdominal imaging.
• Familiarity with AI model evaluation and data harmonization techniques.
• Previous publications or abstracts in radiology or AI-focused research.

You’ll be part of a mission-driven team developing AI solutions to address one of the deadliest cancers. Our lab is highly collaborative, internationally diverse, and actively engaged in cutting‑edge research supported by strong institutional and external partnerships (e.g., Microsoft, Lustgarten Foundation). You’ll also play a central role in building and supporting a growing team.

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The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University’s good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate’s geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

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