Undergraduate Degree Internship - Gastroenterology Research

The primary purpose of the Undergraduate Internship is to provide an intern with relevant field experiences in their chosen field of study. One or more mentors will be assigned to the intern to provide instruction, guidance and to assess performance. 

The intern is responsible for observing the functions and processes of the assigned work unit. In addition, the intern may be given selected work assignments to reinforce the learning experience, to assess the performance levels and to provide benefit to the work unit. The intern will be responsible for completing all internship-related assignments and reports for both Mayo and the participating educational facility. 

The duration and timing of the internship will be arranged with the intern and the sponsoring educational facility. Placement in our internship program does not provide financial assistance with relocation, housing, or transportation. Interns must pass a pre-employment drug test and criminal background check.

The incumbent must be enrolled in an undergraduate degree program from an accredited college or university and actively registered for coursework in the current or academic term.  Proof of enrollment in classes (e.g., enrollment verification, class schedule, or official documentation from the institution) is required and will be verified prior to the start date.

The internship would occur after the student has taken most of the courses required by the degree program. It is open to individuals who are authorized to work in the United States and can remain authorized throughout the internship without the need for Mayo Clinic sponsorship now or in the future, such as U.S. citizens, U.S. nationals, lawful permanent residents, refugees, or asylees.

Preferred Qualifications:

• Coursework or project experience in computer science, artificial intelligence, machine learning, data science, or a related area.
• Proficiency in Python and familiarity with common machine learning and data analysis libraries such as scikit-learn, PyTorch, TensorFlow, pandas, or NumPy.
• Experience or coursework involving machine learning model development, data preprocessing, feature engineering, model evaluation, and visualization.
• Familiarity with one or more areas relevant to the project, such as natural language processing/large language models, deep learning, computer vision, bioinformatics, or healthcare data analytics. These areas align with the project’s clinical prediction, EHR phenotyping, and image-analysis components.
• Ability to work collaboratively with faculty, students, clinicians, and researchers in an interdisciplinary environment, with good communication, documentation, and problem-solving skills.