PhD & Masters University Grad Position-
Our company vision is to amplify human potential. Our mission is to deliver enterprise a powerful tool for transformationan augmented reality platform of great utility & simplicity. Achieving our goals requires passion & dedication. Thats why were committed to building & empowering a diverse team of incredibly talented people & fostering an inclusive culture through our values of unity, innovation, & user centricity.
Magic Leap is looking for PhD & Masters students to join our team for a 6 month Internship Program in Zurich, Switzerland to join our geometric Computer Vision team.
You will work fully integrated in Magic Leaps perception team & help shape the perception systems of our newest generation devices.
Depending on your interest & skills you will work on one or several projects in the area of:
- SLAM / Collaborative SLAM / Visual-Inertial Pose Tracking / Localization
Design & implement advanced algorithms for estimating the 6 DOF pose of our Magic Leap devices by building consistent large scale maps of the environment, optimally fusing visual & inertial measurements collected from multiple cameras & IMU, & performing localization into previously created maps. Challenge the current pipelines with new approaches & help to make it the best in class in AR.
- Marker & Constellation Tracking
Get the most out of the powerful sensor suite of ML2 to track fiducials with state of the art algorithms in order to unlock highly accurate AR guidance use-cases in medtech, manufacturing, & other industries. Take the whole system into account as opposed to only looking into the algorithms. Help integrating with the SLAM systems to become best in class for AR.
- You have just finished or are finalizing your MSc or PhD degree in Computer Science, Robotics, Electrical Engineering or equivalent
- Fluent in C/C++ & Python
- Computer Vision & 3D geometry knowledge
- Projects in Visual SLAM, Visual Inertial Odometry / Sensor Fusion, 3D reconstruction, camera calibration
A plus to have
- Deep networks for geometric computer vision problems using unsupervised & self-supervised approaches
- Software optimization, embedded programming, & parallel computing (e.g. SIMD, OpenCL, GPGPU, etc.)
- MSc or PhD degree in Computer Science, Robotics, Electrical Engineering or equivalent