Student Assistant - Pathology

Job Title:

Student Assistant - Pathology

Medicine | Pathology-JM

The AI4

Path Lab is seeking a highly motivated Student Assistant 2 to assist in the development, testing, and validation of artificial intelligence and deep learning models applied to digital pathology and precision oncology.

The student will work closely with the Principal Investigator and lab researchers to analyze whole-slide histopathology images, manage high-performance data pipelines, and conduct literature reviews. This position is ideal for undergraduate or early graduate students looking to gain hands‑on experience at the intersection of computer science, data science, and cancer research.

• Assist in pulling, organizing, and preprocessing large datasets of whole-slide images (WSIs). Implement image tiling, normalization, and quality control pipelines.
• Support the implementation and training of deep learning architectures such as convolutional neural networks and vision transformers for tasks such as cellular segmentation, glandular architecture analysis, and disease recurrence prediction.
• Conduct targeted literature reviews on emerging topics in computational pathology, oncology, and deep learning methods to support ongoing manuscript and grant preparation.
• Document code, maintain shared repositories (Git Hub), and ensure research reproducibility.
• Participate in weekly lab meetings, present project updates, and collaborate with multidisciplinary team members.

• Currently pursuing or recently completed a bachelor’s degree in computer science, biomedical engineering, data science, bioinformatics, or a closely related quantitative field.
• Basic programming skills in Python.
• Familiarity with machine learning and deep learning frameworks such as PyTorch or Tensor Flow.
• Excellent communication skills and the ability to work independently and collaboratively within a multidisciplinary team.

• Experience with digital pathology libraries such as Open Slide or HistomicsTK or general medical image processing.
• Familiarity with high-performance computing (HPC) environments or GPU acceleration.
• A basic understanding of oncology, biology, or clinical data concepts.

By the end of this internship, the student will have:

• Gained direct experience applying state-of-the-art AI methods to real‑world, clinical cancer data.
• Developed a strong understanding of the digital pathology ecosystem and institutional research pipelines, including IRB and Honest Broker data workflows.
• Had the opportunity to contribute data or writing toward peer‑reviewed scientific abstracts or manuscripts.

Pelotonia Research Center (1040)

Temporary (Fixed Term)

40

First Shift

Final candidates are subject to successful completion of a background check. A drug screen or physical may be required during the post‑offer process.

The university is an equal opportunity employer, including veterans and disability.