Autonomy Algorithm Engineer

Eligibility: Candidates must be nationals of an EU member state, NATO member state, or NATO-allied
partner nation. Required due to defense/dual-use classification of projects (EU Dual-Use Regulation
2021/821, ITAR).

Develop the core state estimation, navigation, control, and mission-level autonomy algorithms enabling fully
autonomous UAV flight. Covers SLAM, Visual-Inertial Odometry (VIO), Kalman filter design, GNSS-denied
navigation, and mission-level behavioral autonomy for operations in contested, GPS-degraded, and
communications-denied environments.

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State Estimation & GNSS-Denied Navigation:
- Design and implement EKF/UKF-based and factor-graph state estimators integrating IMU, GPS, vision,
barometer, and RF positioning for robust 6-DOF pose estimation.

- Develop and tune VIO and visual SLAM pipelines (ORB-SLAM3, VINS-Fusion, LIO-SAM, or custom) for
accurate localization in GPS-denied and contested environments.
- Implement resilient localization strategies that gracefully degrade and recover across sensor dropout
scenarios, including GPS jamming and spoofing conditions.

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Flight Dynamics & Trajectory Algorithms:
- Develop motion prediction, trajectory estimation, and adaptive filtering for dynamic flight conditions, sensor
degradation, and environmental uncertainty.
- Create control-theoretic estimators supporting target tracking, collision avoidance, and cooperative swarm
behaviors.
- Design and validate observer/estimator architectures interfacing with flight control units (FCUs) via
MAVLink/PX4.

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Mission-Level Behavioral Autonomy:
- Design mission-level autonomy behaviors: persistent ISR loitering patterns, autonomous threat-response
replanning, and return-to-safe-area protocols under sensor failure or communications blackout.
- Implement mission re-planning under uncertainty — adapting navigation and task objectives in response to
degraded sensors, emerging threats, or dynamic target assignments.
- Develop time-critical autonomous terminal guidance algorithms for precision terminal-phase operations,
including proportional/augmented navigation under high-dynamic flight conditions.
- Integrate geofencing, airspace deconfliction logic, and dynamic no-fly zone enforcement compliant with EASA
U-Space requirements.

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Integration & Validation:
- Integrate state estimation modules with flight control stacks (PX4/ArduPilot) and ROS2-based autonomy
pipelines.
- Validate algorithms in simulation (Gazebo, MATLAB/Simulink, PX4 SITL) and HIL/HITL environments before
field trials.
- Analyze telemetry, measure KPIs (localization drift, convergence time, robustness to sensor failure), and
iterate improvements.
- Document algorithm designs, mathematical derivations, performance characteristics, and integration
interfaces; support certification efforts.

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Required Qualifications & Experience:

- Master's or PhD in Robotics, Control Systems, Electrical Engineering, Computer Science, or related field.
- 3+ years developing state estimation or autonomous navigation algorithms for UAVs, robotics, or autonomous
vehicles.
- Proven expertise in Kalman filter design (EKF, UKF), sensor fusion architectures, and probabilistic motion
models.
- Deep knowledge of SLAM (visual, LiDAR, or sensor-fusion variants) and visual odometry pipelines.
- Proficiency in C++ and C (Eigen, Ceres, GTSAM) and Python (NumPy, SciPy) for algorithm development.
- Applied experience with simulation tools (MATLAB/Simulink, Gazebo, PX4 SITL) and hardware-in-the-loop
testing.
- Familiarity with flight control stacks (PX4/ArduPilot) and MAVLink protocol.
- Strong mathematical foundation in linear algebra, probability theory, numerical methods, and control theory.
- Solid version control practices (Git/GitLab).

- English: Upper Intermediate or higher.
- Good communication skills and ability to cooperate with adjacent engineering teams.
- National from a NATO member country or one of the following NATO Indo-Pacific partners: Australia, Japan,
South Korea, New Zealand or Ukraine.
- Free criminal record

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Preferred Qualifications & Experience:
- Proficiency in ROS2: package development, TF trees, sensor/message integration, data pipelines.
- Experience with swarm coordination, multi-agent localization, and distributed optimization algorithms.
- Familiarity with tightly-coupled VIO systems (VINS-Mono, Kimera, OpenVINS) and resilient GNSS-denied
approaches.
- Exposure to factor graph optimization libraries (GTSAM, g2o) for batch and incremental SLAM.
- Experience with RF-based positioning (UWB, pseudolite) as supplementary navigation sources.
- Experience with real-time operating systems (FreeRTOS, Zephyr, NuttX) and hard real-time constraint design
for estimation loops.
- Familiarity with safety-critical standards: DO-178C (software levels DAL A–C), MISRA C/C++, and STANAG
4671 for UAV autonomy modules.
- Track record of publication at ICRA, IROS, IEEE Transactions on Robotics, or equivalent; open-source
contributions to SLAM/estimation projects.

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