09/23/2026 updated
100 % available
Robotics Control & Estimation Engineer (PhD) | UAV autonomy, sensor fusion, navigation without GNSS
Geldrop, Netherlands
Worldwide
PhD in Robotics Systems and Control, Eindhoven University of Technology (TU/e)About me
PhD (TU Eindhoven) in estimation and control for quadcopters. Lead control engineer at Avular (drones, AMRs) and Microsure (surgical robotics). State estimation, sensor fusion, flight control, navigation without GNSS, sim-to-real. B2B, remote NL, on-site blocks to 2 weeks.
Automated Guided Vehicle (Unmanned Ground Vehicles)C++ (Programming Language)LinuxGeneralized Linear ModelHardware-In-The-Loop SimulationLinear AlgebraMATLABMotion ControlsMotion PlanningPID ControllersRapid Control PrototypingRobot FrameworkRobotics Design and ProductionRobotic SurgerySimulations
Navigation in GNSS-Denied and Contested Environments
Estimation and navigation when GNSS is degraded, jammed or
absent: adaptive complementary filtering for IMU-based attitude
estimation, inertial navigation, dead reckoning, Markov Jump
Linear Systems for state estimation under intermittent
measurements. PhD subject, TU Eindhoven.
Simulation, HIL and Sim-to-Real Validation
6-DoF and 3-DoF simulation model build and validation, Monte
Carlo dispersion analysis, hardware-in-the-loop testing,
MATLAB/Simulink, Gazebo, PX4, closing the gap between simulation
performance and measured hardware behaviour.
Flight Control and UAV Autonomy
Attitude, position and trajectory control for quadcopters and
VTOL platforms. PX4 and EKF2, autopilot integration, flight
envelope limits including vortex ring state avoidance in descent,
Lyapunov-based target tracking for moving targets.
Control & Estimation Algorithms
Expertise in designing and deploying real-time control and estimation algorithms, including PID, MPC, Kalman filtering, adaptive filters, and Lyapunov-based tracking controllers for robotic and autonomous systems.
Robotics Software Development
Proficiency in full-stack robotics software development using C++, Python, ROS2, Simulink, Linux, and Docker, covering everything from algorithm design and simulation to field-ready deployment.
Signal Processing & System Integration
Strong background in signal processing, frequency-domain analysis (FRFs), loop shaping, real-time system integration, and feedback loop optimization for real-world robotic platforms.
Model Predictive Control (MPC)
Development and implementation of Model Predictive Control algorithms, including globally asymptotically stable trajectory tracking for quadcopters using cascaded control schemes.
State Estimation & Sensor Fusion
Construction of estimation and identification methods for complex systems such as Markov Jump Linear Systems, and development of adaptive complementary filters for IMU-based attitude estimation.
Embedded Control & Hardware Integration
Experience integrating hardware, embedded control, computer vision, and AI into reliable, field-ready robotic solutions, including work on quadcopters and driving robot platforms.
Motion Planning & Trajectory Optimization
Research and implementation of optimization-based motion planning and trajectory optimization algorithms for robotic manipulators and aerial systems, including energy-optimal and time-optimal descent maneuvers.
Experience
Professional experience10+ years
Freelancer since2024
Languages
GermanBasic knowledgeEnglishFluentDutchNative speakerSpanishBasic knowledge
Project history
Designing, building, and integrating end-to-end robotic systems, combining hardware, embedded control, computer vision, and AI into reliable, field-ready solutions. Leading technical teams and steering development from concept to validated prototype. Qualified among the 30 advancing teams in Intrinsic's AI-for-Industry challenge (contact-rich manipulation).
Independent consulting on control, estimation, and deployment for robotic systems. Designed and implemented a Lyapunov-based tracking controller for an autonomous aerial system, replacing a hardcoded trajectory.
Leading the control team to achieve performance, stability and robustness targets for the medical robotic system. Responsibilities include planning of team activities, coaching team members, providing input to upper management, and actively developing control algorithms and documentation.