Validation-Driven
Focusing on physical truth. Verifying filter models with real-world sensor logs and hardware telemetry.
About Daniel John Chadwick
Researcher and engineer focused on resilient navigation, embedded estimation, and practical engineering systems.
Daniel John Chadwick is a researcher and engineer working across inertial navigation, sensor fusion, embedded estimation, MEMS IMU calibration, and practical engineering software.
With a background in building calibration tools, data pipelines, and automation hardware, I focus on creating high-reliability systems that bridge physical sensors and software architectures.
My research interests center around Particle Filters, Extended/Unscented Kalman Filters, and Factor Graph Optimization techniques. I am particularly focused on dynamic calibration methods that can run directly on embedded hardware to correct MEMS IMU sensor errors on-the-fly.
Methodology
Core Principles
Embedded Efficiency
Optimizing matrix operations and estimation loops to run in real-time on low-power hardware.
Open & Reproducible
Maintaining modular structures, documented configurations, and clean dependencies for collaborative research.
Experience
Employment History
Data Scientist & Software Developer (Part-time)
2026 – PresentCarbon Happy World
Developing green-tech software applications, carbon estimation models, and analytics platforms concurrent with PhD research.
Contractor — Automation Leader & Data Scientist (Part-time)
2023 – 2026Procter & Gamble UK
Led automation initiatives and built predictive data analytics pipelines, transitioning to part-time in 2024 to support PhD research.