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Niche PX4/robotics ML skillset reduces qualified applicant pool despite metro location.
Strong PX4/UAV and embedded ML focus limits transferability outside robotics and aerospace domains.
Mandatory PX4, ML and embedded development skills create stringent technical screening for candidates.
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Research, design, and integrate AI-based flight control algorithms (neural network models including LSTM and GRU) into the PX4 Autopilot framework for UAVs.
Support deployment and validation of AI-integrated flight controllers using PX4 SITL/Gazebo simulation and real quadcopter hardware.
Document integration workflow, implementation details, and validation results for the AI-based UAV control system.
Working knowledge of the PX4 Autopilot framework and its development environment is mandatory.
Strong programming skills in Python and C/C++.
Basic understanding of control systems, machine learning, recurrent neural networks (LSTM, GRU), and familiarity with Reinforcement Learning concepts.
Education: Final-year or current undergraduate/postgraduate student in Aerospace Engineering, Robotics, Computer Science, Electronics, Electrical Engineering, AI, or related fields.
Has hands-on experience or relevant projects involving PX4, UAVs, robotics, or AI-based flight control systems.
Capable of independently researching and implementing complex technical solutions within open-source UAV/autopilot software ecosystems.
Comfortable working with simulation environments (PX4 SITL, Gazebo) and validating algorithms on embedded flight control hardware.