Imagine working on a precision inertial sensor built around a mechanically resonant sensing element, where measurement quality depends on keeping that element oscillating at exactly the right frequency and amplitude and extracting a clean, reliable signal. As a Digital Signal Processing Engineer, you will own the algorithms that make this possible, defining control loops, building estimation and compensation methods, and establishing mathematical models to understand and validate performance.
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Tasks
- Design and simulate control loops to keep the sensing element stable and locked, including frequency tracking through PLL, amplitude control through AGC, and quadrature nulling.
- Define loop architectures, bandwidths, gains, stability margins, and other control parameters based on sensor physics and system-level performance requirements.
- Develop slower outer-loop algorithms for bias estimation, thermal compensation, and state estimation, ideally implementing them in maintainable C/C++ for embedded targets.
- Design and implement Kalman filtering approaches to transform the output of the locked sensing element into an accurate and usable rate measurement.
- Own the co-simulation environment used to compare hardware implementation against the physics-based reference model and identify sources of discrepancies.
- Build mathematical and simulation models in Python or MATLAB to explore sensor behaviour, test algorithms, and validate design decisions before implementation.
- Establish and interpret sensor characterisation methods including Allan variance, bias instability, angular random walk, scale-factor linearity, and related performance metrics.
- Work closely with electronics, FPGA, embedded software, physics, and systems engineers to translate mathematical models into robust real-world implementations.
- Investigate unexpected sensor behaviour from first principles, connecting physical effects, measurements, models, and algorithms to find the underlying cause.
Requirements
- B.Sc., M.Sc. or PhD in Computer Science, Applied Mathematics, Applied Physics, Control Theory, Electrical Engineering, or a closely related technical field.
- Strong foundation in control theory and digital signal processing, including closed-loop design, stability and phase margin, discrete-time filter design, demodulation, I/Q processing, and phase-locked loops.
- Practical experience with state estimation and Kalman filtering, including linear, extended, or adaptive approaches applied to real physical systems.
- Strong Python or MATLAB skills for mathematical modelling, simulation, data analysis, and algorithm development.
- Ability to translate complex physical behaviour into clear mathematical models and scalable algorithmic solutions.
- Confidence working from first principles, including reading technical literature, extracting the underlying model, challenging assumptions, and turning theory into a working simulation.
- Ability to work effectively across disciplines and communicate complex mathematical concepts to engineers working on hardware, FPGA, embedded software, and system-level development.
- Experience writing maintainable C/C++ for embedded targets is strongly preferred.
- Previous experience with resonant sensors, MEMS inertial sensors, vibratory gyroscopes, or similar sensing technologies is a strong advantage.
- Familiarity with inertial navigation, sensor fusion, or GNSS-denied navigation is an advantage.
- Experience within aerospace, defence, robotics, or another high-performance engineering environment is a plus.
- Bare-metal or RTOS embedded development experience is a plus.
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Das Team
Work closely with electronics, FPGA, embedded software, physics, and systems engineers to translate mathematical models into robust real-world implementations.