- Experience in 2D and 3D medical imaging (CT, MRI, Ultrasound, TEE) and DICOM data handling.
- Strong understanding of 3D geometry, camera calibration, stereo vision, and multi-view reconstruction.
- Experience in segmentation, registration, and object tracking within medical image contexts.
- Proficiency with classical computer vision techniques (OpenCV, PCL, feature detection, structure-from-motion, SLAM, etc.).
- Knowledge of generative and reconstruction models (GANs, VAEs, Diffusion Models) and fine-tuning methods for domain-specific applications.
- Design, develop, and optimize AI/ML algorithms for medical image analysis, segmentation, and 3D reconstruction from TEE and CT images.
- Research and implement advanced deep learning architectures including CNNs, GANs, VAEs, and Diffusion Models for medical imaging tasks.
- Develop robust 3D reconstruction pipelines from 2D image data and multi-view geometries, tailored to medical imaging workflows.
- Perform multimodal image registration (CT-CT, CT-MRI, Fluro-Endo, 2D-3D) and develop tools for alignment, calibration, and fusion.
- Enhance and denoise medical images using advanced computer vision and AI-based enhancement techniques.
- Work extensively with DICOM data, integrating with PACS systems for data ingestion and retrieval.
- Collaborate with teams for dataset curation, labeling, and ground truth generation.
- Develop scalable training and inference pipelines on cloud platforms (AWS preferred; Azure/GCP acceptable).
- Ensure reproducibility and traceability in experiments using MLOps practices (Docker, MLflow, or similar).
- Collaborate with software engineers to integrate AI components into production-grade imaging applications.
- Document research findings, maintain version-controlled repositories, and contribute to technical publications or IP filings.
- Stay up-to-date with emerging trends in AI, computer vision, and medical imaging technologies.