spaice-tech - GNC Engineer

• M.S. in Aerospace Engineering, Robotics, Control & Optimization, or a related field. • Expertise in at least two of the following: state estimation (Kalman filtering, nonlinear/batch estimation), optimal control, trajectory optimization, robust control (e.g. tube MPC), multi-agent coordination and distributed control. • Strong coding ability in C++ and Python, with experience implementing and validating estimation and control algorithms. • A solid grasp of dynamics and the relevant math (linear algebra, probability and statistics, optimization) that estimation and control build on. • Experience designing and running simulations to develop, test, and validate GNC algorithms before they reach hardware. • Demonstrated ability to deliver well-tested, reliable work in collaborative, fast-moving environments. • Industry placements or working experience. • Hands-on experience working with drones. • Familiarity with flight stacks and protocols such as PX4, ArduPilot, and MAVLink. • Familiarity with real-time embedded computing, flight software, or running estimation/control algorithms on resource-constrained hardware. • Familiarity with optimization toolchains for real-time control (e.g. acados, CasADi, OSQP, CVXPY). • Familiarity with data-driven or learning-based methods for estimation and control (e.g. reinforcement learning, learning-based MPC, system identification). • Background in orbital dynamics. • 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).