Lead Researcher focused on advanced control systems for energy applications
Responsibilities
Lead the design, development, and deployment of advanced control strategies including model based control (MBC), adaptive control, robust control, and nonlinear control for complex energy systems.
Architect control system frameworks for dynamic systems, ensuring stability, performance, scalability, and seamless integration with hardware and software platforms.
Lead system modelling, simulation, and hardware-in-the-loop (HIL) testing to validate and refine control algorithms before field deployment.
Drive the application of signal processing techniques for sensor data conditioning, state estimation, and fault detection to support control system performance.
Direct the analysis of complex operational datasets to inform control design decisions and guide the development of data-driven control and optimization algorithms.
Requirements
PhD in control systems engineering or related discipline
Master's degree in control systems engineering or related discipline
Lead Researcher focused on advanced control systems for energy applications
Responsibilities
Lead the design, development, and deployment of advanced control strategies including model based control (MBC), adaptive control, robust control, and nonlinear control for complex energy systems.
Architect control system frameworks for dynamic systems, ensuring stability, performance, scalability, and seamless integration with hardware and software platforms.
Lead system modelling, simulation, and hardware-in-the-loop (HIL) testing to validate and refine control algorithms before field deployment.
Drive the application of signal processing techniques for sensor data conditioning, state estimation, and fault detection to support control system performance.
Direct the analysis of complex operational datasets to inform control design decisions and guide the development of data-driven control and optimization algorithms.
Requirements
PhD in control systems engineering or related field
Master's degree in control systems engineering or related field
Design and develop high-precision navigation and positioning solutions based on tightly-coupled IMU/GNSS filtering, improving localisation accuracy, integrity, and robustness to jamming and spoofing for autonomous systems.
Responsibilities
Drive the architecture and implementation of navigation / PNT solutions for autonomous platforms
Develop navigation resilient to GNSS degradation, jamming and spoofing (anti-jam / anti-spoof)
Design multi-sensor fusion (GNSS, INS/IMU, RF, vision) and the associated estimation and filtering algorithms
Design test plans (e.g. GNSS receiver testing in jamming environments), run simulations and replays, and report on findings and performance against targets
Collaborate across HW, software, EW and ML teams on technical interfaces and system-level trade-offs
Requirements
MSc in Engineering (aerospace, robotics, EE or related)
PhD (plus)
Nice to have: RF constraints (GNSS jamming / spoofing)
Lead the development, integration, and flight validation of navigation systems for autonomous aircraft and related defense platforms.
Compensation
$250,000—$280,000 USD
Responsibilities
Own the technical architecture, roadmap, execution, and delivery of navigation capabilities
Translate mission objectives into navigation requirements and performance budgets
Design, analyze, and mature navigation algorithms
Develop robust estimation solutions
Establish rigorous evaluation methods
Requirements
Bachelor's degree in aerospace engineering, mechanical engineering, electrical engineering, robotics, mathematics, physics, or a related technical field
Nice to have: Master's degree or Ph.D.
Nice to have: advanced estimation techniques such as extended and unscented Kalman filtering
Nice to have: GNSS-denied or GNSS-degraded navigation
Responsible for the design, development, testing and validation of the high-level control system for a powered leg exoskeleton, including gait phase estimation, activity detection, and natural assistance delivery.
Responsibilities
Own the high-level exoskeleton control architecture, from sensing and state estimation to torque commands
Develop and tune gait phase estimation, activity recognition and transition detection using IMU, encoder and torque sensor data
Design assistance strategies and torque profiles that adapt to users, speeds, and terrain
Explore and deploy learning-based control methods on embedded hardware
Build and maintain simulation and modeling tools like MuJoCo, OpenSim, MATLAB/Simulink
Lead design and implementation of high-fidelity fusion algorithms for autonomous truck localization and mapping.
Responsibilities
Lead the design and evolution of our localization architecture, making critical build-vs-buy decisions and defining long-term technical roadmaps.
Design and implement robust, real-time algorithms for sensor fusion, visual/LiDAR odometry, and global localization using techniques like Factor Graphs, EKF/UKF, and particle filters.
Develop pipelines to generate, maintain, and query High-Definition (HD) maps.
Work on priors-based localization and semantic map matching.
Solve the stale map problem by designing systems for change detection, using fleet data to identify construction zones, lane shifts, or new signage and update the map in near real-time.
Requirements
M.S. or Ph.D. in Robotics, Computer Science, Electrical Engineering, or a related field
Nice to have: shipping localization stacks for deployed autonomous vehicles or mobile robots
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