2026 Fall Internship / Artificial Intelligence in Healthcare
We develop systems that create solutions to real-world problems at the intersection of healthcare technologies and artificial intelligence. As we continue to advance our AI-focused R&D efforts in healthcare, including medical image analysis, DICOM data processing, disease/risk modeling, and clinical decision support systems, we are looking for intern teammates who will take an active role in this process.
We aim to work with candidates who will join our R&D team, have a strong motivation to learn, want to gain technical depth, and are interested in developing AI-based solutions in the healthcare field.
What Will You Work On?
Medical Image Processing:
Contributing to data cleaning, metadata analysis, filtering, preprocessing, and normalization processes on medical datasets in DICOM format.
Machine Learning and Deep Learning:
Supporting the development of disease detection, image-based analysis, and risk prediction models using tools such as MONAI, FAISS, and similar frameworks.
Current AI Approaches:
Taking part in research and prototyping activities focused on applying current AI approaches such as Agentic AI, LLM/LAM, and RAG architectures to healthcare technologies.
Performance-Oriented Development:
Developing optimized, sustainable, and scalable Python solutions by applying knowledge of algorithms and data structures.
Technical and Personal Competencies
- 3rd or 4th-year student in Computer Engineering, Artificial Intelligence, Software Engineering, or a related field
- Good proficiency in Python
- Strong foundation in algorithms and data structures
- Interest in machine learning, deep learning, and computer vision
- Following the artificial intelligence literature
- Familiarity with platforms such as GitHub, Kaggle, or similar environments
- Analytical mindset, problem-solving orientation, and attention to detail
Nice-to-Have Qualifications
- Interest in medical AI, healthcare informatics, or medical image processing
- Familiarity with advanced AI approaches such as local language models, MoE, RAG, or Agentic AI
- Academic projects, open-source contributions, Kaggle experience, or a personal project portfolio
- Previous experience with DICOM, medical data formats, or clinical data processing workflows