• Currently pursuing or recently completed a B.S., M.S., or PhD in Aerospace Engineering, Robotics, Control & Optimization, or a related field.
• Familiarity with one or more of the following, through coursework, research, or projects: state estimation (e.g. Kalman filtering), optimal control, trajectory optimization, robust control, multi-agent coordination.
• Comfortable coding in C++ and/or Python, with some experience implementing algorithms and turning math into working code.
• A solid grasp of the fundamentals of dynamics and the relevant math (linear algebra, probability and statistics, optimization) that estimation and control build on.
• Curiosity, initiative, and the ability to learn quickly in a collaborative, fast-moving environment.
• Prior internship, research, or hands-on project experience in GNC, robotics, or a related area.
• Hands-on experience working with drones.
• Familiarity with flight stacks and protocols such as PX4, ArduPilot, and MAVLink.
• Exposure to real-time embedded computing, flight software, or running algorithms on resource-constrained hardware.
• Familiarity with data-driven or learning-based methods for estimation and control (e.g. reinforcement learning, learning-based MPC).
• Background in orbital dynamics.
• A strong course project, thesis, or publication in estimation, control, or autonomous navigation.
• Publications in estimation, control, or autonomous navigation for aerospace or robotics, in journals and conferences (e.g. ACC, CDC, AIAA SciTech, IROS, ICRA, TAC, Automatica).