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Research Fellow DLMP Han lab

Mayo Clinic


Job Location:

Rochester, NH - USA

Monthly Salary: Not provided by the employer
Posted: 5 August 2026 (30+ days ago)
Application Deadline: 18 November 2026
Vacancies: 1 Vacancy

Job Summary

Description

The Research Fellow will work on new and ongoing projects in the Division of Computational Pathology and Artificial Intelligence. Projects will provide an excellent opportunity to work with multidisciplinary teams and large clinical data sets.

Mayo Clinic has embarked on digitizing its large collection of glass slides with several million gigapixel images currently available. The research fellow will use these data sets accompanied by other clinically sourced data as a springboard to pursue multiple cutting-edge topics in machine learning image analysis and discovery in support of the clinical and research activities of the division. The fellow will work on projects that include:

  • Developing novel algorithms to classify segment characterize or predict patient outcomes biomarker status or disease
  • Evaluating model behavior and robustness with a focus on developing novel methods to improve model performance and generalizability
  • Analyzing human-AI interaction to understand the human factors associated with AI augmentation of pathology

The research fellow will work under the mentorship of pathologists and data scientists and will work closely with other trainees IT groups and other staff in support of these projects.

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. Proof of English proficiency is required for J-1 Short-Term Scholars Research Scholars Professors Specialists and Student Interns sponsored by Mayo Clinic.



Qualifications

Must have a Ph.D. M.D. or equivalent doctoral degree in a field deemed relevant by the program.




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