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Microsoft Applied Sciences Intern 
United Kingdom, England, London 
820025700

Yesterday

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Recent advances in AI have been driven in great part by developments in computer vision. However, geometric computer vision, a subarea of great importance to key applications such as autonomous driving, robotics, and augmented reality, has not benefited from this revolution - today, most problems in geometric computer vision are still solved through classical methods. In this project, we will explore how methods based on the formalism of geometric algebra can help in closing this gap. Specific topics include:
  • Geometric algebra methods for deep optical flow computation.
  • Direct application of deep learning in geometric algebra estimation.
Qualifications

Required Qualifications:

  • Must be registered to a PHD program.
Responsibilities

  • 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.

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