Datascientist -3D Reconstruction Engineer - Cardiology

We are seeking a talented  3D Reconstruction Engineer  to join our team. In this role, you will be responsible for building and optimizing 3D modelsof blood vessels using angiogram image data, contributing to innovative solutions in the medical imaging space. You will initially focus on point cloud techniques for 3D vessel reconstruction, and as more data becomes available, you will lead the transition towards machine learning andgenerative modeling techniques to improve the accuracy, scalability, and automation of our models.

Responsibilities

• Develop and optimize algorithms for 3D reconstruction of blood vessels from medical imaging (e.g., coronary angiograms).
• Implement point cloud-based reconstruction techniques for modeling complex vessel geometries.
• Lead the transition from point cloud and deterministic methods to advanced machine learning techniques, integrating neural networks, deep learning, and generative models to improve 3D reconstructions.
• Research and apply state-of-the-art machine learning techniques, such as Generative Adversarial Networks (GANs) or autoencoders, tomodel vessel geometries more accurately and efficiently.
• Collaborate with cross-functional teams to ensure smooth integration of new reconstruction methods into AngioInsight's diagnostic software.
• Scale the 3D reconstruction pipeline using AI-driven techniques, particularly as the dataset grows in size and complexity.
• Enhance the performance and accuracy of existing 3D models using machine learning-based refinements.
• Conduct research on emerging trends in AI-driven 3D modeling and medical imaging to continuously evolve the reconstruction techniques.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Biomedical Engineering, Applied Mathematics, Robotics or related field.
• Experience in Robotics, SLAM, SfM, or stereo vision
• Proven experience with 3D reconstruction techniques, particularly point clouds, and geometry representation methods.
• Strong background in machine learning techniques, particularly neural networks, deep learning, and generative models.
• Proficiency in programming languages such as Python, C++, or MATLAB, and experience with machine learning frameworks like PyTorch or TensorFlow.
• Experience with medical imaging data (e.g., DICOM, angiograms) and working in the healthcare/medical technology space is a strong plus.
• Strong problem-solving skills with the ability to transition from deterministic methods to machine learning approaches.
• Excellent communication and teamwork skills to collaborate with cross-disciplinary teams, including data scientists, software engineers, and clinicians.