Lúnasa is seeking a Computer Vision engineer who will support the development with definition, design, analysis, and implementation of the visual navigation architecture related to the satellite rendezvous proximity operations and docking (RPOD) mission, powered by the intelligent visual-based GNC system in-development at Lúnasa. The successful applicant will be involved from early stages in the definition and analysis of the system requirements for Lúnasa’s in-orbit servicing vehicle, including close proximity and docking applications.
- Design and develop novel computer vision and/or Machine Learning algorithms in areas such as: real-time scene and rigid object tracking, space object detection, 6 DoF pose estimation, ley point estimation, and depth sensing.
- Develop prototypes for a Computer Vision subsystem for spacecraft GNC applications including active/passive marker design, drive continued development, and integrate robust solutions into products.
- Collaborate with Lúnasa’s Guidance, Navigation, and Control engineering and research teams in Computer Vision, Machine Learning, and graphics (Digital Twin).
- BSc degree in Computer Science, Computer Vision, Machine Learning, or related degrees.
- 2+ years of experience developing and designing computer vision and/or machine learning technologies and systems.
- 3+ years of experience engineering in C++ and/or python (including university projects).
- Prototyping and engineering experience in at least one relevant specialisation area in either Computer Vision or Machine Learning: Marker-based rigid body pose estimation, Extended Kalman Filter for Rigid Body Pose Estimation, SLAM, State Estimation Sensor Fusion, Dense 3D reconstruction, object detection, segmentation and tracking scene understanding/ Semantic segmentation, Photorealistic rendering, Camera calibration.
- MSc or PhD degree in Computer Science, Computer Vision, Machine Learning, Robotics or related technical field.
- Studied spacecraft systems related modules.
- 2+ years of industry experience working on projects such as Marker-based rigid body pose estimation, Extended Kalman Filter for Rigid Body Pose Estimation, real-time SLAM and 3D reconstruction, sensor fusion and active depth sensing, object tracking and rigid body pose estimation, and/or image processing. Image and/or semantic segmentation, 2D and 3D keypoint estimation, and surface reconstruction, depth estimation, generative methods such as GANs, or photorealistic rendering.
- Developing and designing Computer Vision and/or Machine Learning technologies and systems for running on real-time embedded systems such as autonomous vehicle control, aircraft/drone guidance, navigation, and control.
- Background in Machine Learning with experience in large scale training and evaluation of deep convolutional and/or recurrent neural networks and/or GANs.