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Field
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algorithms from concept to implementation; and performing systems integration and testing for technology maturation. Minimum Qualifications: 1. Ph.D. in relevant technical field 2. 6 years experience in
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vehicle (AV), allowing for automated detection, prediction, mapping, and planning. During the vehicle’s operation, data is obtained through a myriad of sensors in an AV—including RADAR, LIDAR, cameras, and
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will work in a group with other students and take on specific tasks. The aim is to analyse the robot's capabilities and to implement algorithms that enable the robot to be used sensibly in applications
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will work in a group with other students and take on specific tasks. The aim is to analyse the robot's capabilities and to implement algorithms that enable the robot to be used sensibly in applications
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operation. Develop modular architectures for multi-agent coordination, sensing, and communication. Integrate sensor suites, flight controllers, and swarm coordination algorithms into UAV platforms. Conduct
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troubleshooting to ensure real-world readiness. Develop and implement algorithms for autonomous control, SLAM and navigation. Ensure reliable communication between hardware components and the robotic system
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troubleshooting to ensure real-world readiness. Develop and implement algorithms for autonomous control, SLAM and navigation. Ensure reliable communication between hardware components and the robotic system
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of algorithms and digital neuromorphic hardware is an additional avenue for enhancing the efficiency of the methods. In this context the research will explore digital, event-based implementations
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world