Research Associate - Multimodal AI for Oncology
Research Associate - Multimodal AI for Oncology
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Job Description
Responsibilities
We are hiring a Research Associate to work on a project funded by the Mayo Clinic Cancer Center to develop multimodal deep learning models to predict cancer recurrence. We are seeking an exceptional researcher technically outstanding intellectually curious and motivated by real-world impact. You will collaborate with AI scientists and clinicians from across Mayo Clinic but will be embedded within the Radiation Oncology AI & Data Analytics team led by Dr. Andrew Foong ()
You will join a uniquely positioned institution: Mayo Clinic treats over 1.4 million patients annually and is the most trusted name in medicine. The department has access to rich high-quality cancer treatment data and strong institutional support for innovation. Youll help bring cutting-edge AI tools into the clinic and shape how technology improves outcomes for cancer patients.
We are looking for someone who could thrive in top-tier tech companies or academic AI labs but chooses to work here because you want your work to matterto patients to clinicians and to the future of healthcare.
What Youll Do
Create impactful AI: Develop implement evaluate and deploy deep learning models in a real clinical setting.
Conduct ambitious research: Push the frontiers of AI for cancer treatment in collaboration with a multidisciplinary team of computer scientists clinicians and software engineers.
Qualifications
Ph.D. in Computer Science Biomedical Informatics Machine Learning Engineering Physics Medical Physics Applied Math Statistics or a related field.
Strong background in deep learning (e.g. CNNs transformers).
Strong Python and ML ecosystem skills (e.g. PyTorch JAX scikit-learn etc.).
Self-motivated execution-focused and intellectually independent.
Stays goal-oriented over method-oriented and isnt afraid of unglamorous but high-value work when it matters.
Comfortable working with real-world messy heterogeneous healthcare data.
A team player who thrives in interdisciplinary environments.
Required Experience:
IC
Employment Type
Full-Time
