AI & I Data Analytics Research Fellow – Radiation Oncology
At a glance
Mid-level Data Analysis & BI role at Mayo Clinic. Rochester · full-time · no salary stated · we estimate $86,000–$135,000 across the United States.
Mayo Clinic has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Build, deploy and assess machine-learning models in clinical settings, with attention to adoption, usability and patient-care impact
- Help create AI systems for clinical workflows, including prediction, documentation automation and decision support
- Plan and carry out research with physicians, physicists, data scientists and engineers
- Take research from initial ideas through prototypes and into real-world clinical use
What you bring
- A PhD in a listed technical field, such as computer science, machine learning or biomedical informatics
- Strong command of current machine-learning approaches, including deep learning or transformers
- Substantial Python experience and familiarity with ML tools such as PyTorch or JAX
- Clear writing and presentation skills for explaining complex work
Who this fits
This role suits a researcher who can take ownership of open-ended work and carry projects through to execution. You’ll collaborate across clinical and technical disciplines, combining research with implementation in real care settings.
From the employer
The AI and Data Analytics (AIDA) team within the Department of Radiation Oncology at Mayo Clinic is hiring a Research Fellow to help advance the next generation of AI in cancer care: https://radonc-aida.github.io ).
We’re looking for an exceptional researcher that is technically strong, intellectually curious, and motivated by real-world impact.
You’ll join a uniquely positioned institution. Mayo Clinic treats over 1.4 million patients annually and is consistently ranked among the most trusted names in medicine. Within this environment, AIDA operates as a fast-moving, high-impact team building and deploying AI tools directly into clinical workflows.
Over the past year, our tools have moved beyond prototypes into active clinical use, supporting hundreds of clinicians across multiple specialties. Several have been recognized at institutional and national forums, reflecting both their technical innovation and measurable impact on care delivery. This is a rare opportunity to work on AI systems that are not only published, but used, evaluated, and iterated on in real clinical environments.
Whether it’s building models to personalize cancer treatment, extracting insights from longitudinal clinical data, or deploying tools that streamline physician workflows, your work will span both foundational research and real-world translation.
We are looking for someone who could thrive in top-tier tech companies or academia but chooses to work here because you want your work to matter to patients, to clinicians, and to the future of healthcare.
What You’ll Do
Deploy impactful AI: Develop, implement, and evaluate machine learning models in real clinical settings. Success is measured not just by publications, but by adoption, usability, and clinical impact.
Work on end-to-end systems: Contribute to AI tools that integrate into clinical workflows ranging from predictive modeling to automation of documentation and decision support.
Lead ambitious research: Design and execute novel research projects in collaboration with a multidisciplinary team of physicians, physicists, data scientists, and engineers.
Translate research into practice: Help move ideas from concept to prototype to deployment to real-world use, learning what actually works in healthcare.
- PhD in Computer Science, Machine Learning, Biomedical Informatics, Engineering, Physics, Applied Math, Statistics, or a related field.
- Strong background in modern machine learning (e.g., deep learning, transformers, or related methods).
- Strong experience with Python and ML ecosystems (e.g., PyTorch, JAX, scikit-learn).
- Strong communication skills: able to write and present complex ideas clearly.
- Self-motivated, execution-focused, and comfortable owning ambiguous problems.
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