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-enabled clinical research and cancer care: https://radonc-aida.github.io.

We are looking for an exceptional researcher who is intellectually curious, clinically engaged, technically capable, and motivated by real-world impact. Candidates with experience in LLM pipelines, digital pathology, computational pathology, multimodal AI, or the integration of pathology, imaging, and clinical data are especially encouraged to apply.

You will 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 developing, evaluating, and translating AI tools into clinical and research workflows.

Our work sits at the intersection of clinical oncology, biomedical data science, digital pathology, medical imaging, and applied AI. Over the past year, several of our tools have moved beyond prototypes into active clinical use, supporting clinicians across multiple specialties. These tools have been recognized at institutional and national forums for their innovation, usability, 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.

The Research Fellow will contribute to projects focused on understanding and extracting value from complex clinical data, including electronic health records, longitudinal oncology data, clinical notes, pathology reports, digitized pathology images, imaging-derived data, treatment information, and patient outcomes. A major emphasis of the role will be using AI, including large language models and multimodal learning methods, to support clinical data retrieval, cohort discovery, data abstraction, documentation workflows, decision support, biomarker discovery, and translational cancer research.

Whether it is identifying clinically meaningful patterns in cancer treatment data, using LLMs to retrieve and summarize relevant patient information, analyzing digital pathology or imaging data, building tools that support physician workflows, or designing studies that evaluate AI in real-world care settings, your work will span both rigorous research and practical clinical translation.

We are looking for someone who wants to work closely with physicians, physicists, pathologists, data scientists, engineers, and clinical teams to develop AI tools that matter to patients, clinicians, and the future of healthcare.

What You'll Do:

• Advance clinically meaningful AI research: Design and execute research projects that use AI and data analytics to address important questions in radiation oncology, cancer care, digital pathology, and clinical operations.
• Work with real-world clinical data: Analyze and interpret complex clinical datasets, including structured EHR data, clinical notes, pathology reports, digitized pathology images, treatment records, outcomes data, and longitudinal patient information.
• Use LLMs for clinical data retrieval and abstraction: Apply large language models and related methods to retrieve, summarize, structure, and validate information from clinical records, pathology reports, and other healthcare data sources.
• Contribute to multimodal clinical AI: Help develop methods that integrate multiple data types, such as clinical text, structured EHR data, pathology data, imaging data, treatment data, and outcomes.
• Support digital pathology and image-based research: Contribute to AI-enabled analysis of pathology-related data, including whole-slide images, pathology reports, tumor characteristics, biomarkers, and clinicopathologic correlations.
• Translate research into practice: Help move ideas from clinical need to research question, prototype, evaluation, and real-world implementation.
• Develop and evaluate AI tools in clinical settings: Contribute to tools that support documentation, clinical workflow efficiency, cohort identification, decision support, quality improvement, and patient-centered research.
• Collaborate across disciplines: Work closely with radiation oncologists, pathologists, medical physicists, informaticians, data scientists, engineers, and clinical staff to ensure that AI tools are clinically relevant, usable, and responsibly evaluated.
• Measure real-world impact: Evaluate success not only through publications, but also through clinical usefulness, adoption, reliability, workflow integration, and measurable impact on care delivery.
  
  A Research Fellow at Mayo Clinic is a temporary position intended to provide training and education in research. Individuals will train in the research program of a Mayo Clinic principal investigator. Qualified individuals will demonstrate the potential for research as evidenced by their training and peer-reviewed publications and should become competitive for national research grants. Upon background check and CCATS Executive Committee approval, Research Fellow may have patient/research subject contact directly relating to and incidental to the original research program. Proof of English proficiency is required for J-1 Short-Term Scholars, Research Scholars, Professors, Specialists, and Student Interns sponsored by Mayo Clinic.

• MD or PhD in Biomedical Informatics, Computer Science, Machine Learning, Engineering, Physics, Applied Mathematics, Statistics, Epidemiology, Health Data Science, Computational Pathology, Computational Biology, or a related field.
• Strong interest in clinical research, translational AI, and improving cancer care.
• Experience working with clinical, biomedical, imaging, pathology, or real-world healthcare data.
• Experience working in cloud-based clinical data environments, ideally including Google Cloud Platform, BigQuery, and FHIR-based healthcare data resources, to support secure data retrieval, analysis, and translational research workflows.
• Familiarity with machine learning, natural language processing, computer vision, large language models, multimodal AI, or related methods.
• Ability to use AI tools, including LLMs, for tasks such as clinical data retrieval, summarization, abstraction, cohort identification, or workflow support. 
• Experience with Python and common data science or machine learning tools.
• Ability to understand clinical context, ask clinically meaningful questions, and work closely with physicians and other healthcare professionals.
• Strong communication skills, including the ability to explain technical concepts clearly to clinical and non-technical collaborators.
• Self-motivated, collaborative, and comfortable working on open-ended problems in a multidisciplinary research environment.