Key Responsibilities:
- Segmentation and Object Detection: Experimenting with state-of-the-art methods for 2D/3D segmentation and object detection in medical imaging.
- Model Training: Training neural networks on large-scale datasets, specifically in the context of OCT and X-ray angiography.
- Result Analysis: Analyzing outcomes from experiments and developing strategies to enhance model accuracy and scalability.
- Integration and Optimization: Working on the integration of models into existing software systems, focusing on performance profiling and optimization.
- Collaboration: Collaborating across teams and organizations to advance research and implementation efforts.
Required Qualifications:
- Educational Background: An MS or PhD in Engineering, Computer Science, or a related field.
- Experience: A minimum of 5 years of relevant experience (or 2+ years with a PhD).
- Deep Learning Expertise: Extensive experience with deep learning and computer vision techniques.
- Advanced AI/ML Knowledge: Proficiency in foundational models, generative AI, and various learning paradigms.
- Technical Skills: Hands-on experience with frameworks like TensorFlow and PyTorch for deep neural network training.
- Theoretical Knowledge: Strong understanding of computer vision and machine learning principles.
This role is ideal for someone passionate about leveraging AI in medical imaging and who has a solid foundation in both theoretical and applied aspects of machine learning and computer vision. Collaboration and integration skills will also be crucial for successfully advancing the projects.