Student Assistant - Pathology

Responsibilities

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

Requirements

• 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 TensorFlow).
• Excellent communication skills and the ability to work both independently and collaboratively within a multidisciplinary team.

Qualifications

• Desired Qualifications (Nice to Have): Experience with digital pathology libraries (e.g., OpenSlide, 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.

Benefits

• 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).
• Opportunity to contribute data or writing toward peer-reviewed scientific abstracts or manuscripts.