Applied Sciences Intern

• Geometric algebra methods for deep optical flow computation.
• Direct application of deep learning in geometric algebra estimation.

Required Qualifications:

• Must be registered to a PHD program.

• Develop and Implement Deep Learning Models: Design, train, and optimize machine learning and neural network models for applications in computer vision and robotics, ensuring high performance and accuracy.

• Enhance Geometric Computer Vision Techniques: Apply advanced algorithms for tasks such as visual odometry, feature extraction, and object recognition, leveraging geometric principles to improve system robustness and accuracy.

• Implement SLAM (Simultaneous Localization and Mapping): Develop and optimize algorithms for real-time environment mapping and robot localization, ensuring seamless navigation and object tracking in dynamic environments.

• Integrate Optical Flow and Visual Navigation Systems: Design and integrate optical flow methods to enable precise motion estimation, visual-based navigation, and dynamic obstacle avoidance for autonomous systems.

• Leverage Geometric Algebra for Advanced Computing: Utilize geometric algebra (Clifford algebra) to improve the efficiency and accuracy of spatial transformations, rotations, and other geometric operations in computer vision and robotics tasks.