Christian Mollière is a research engineer and team lead specializing in radiometry and applied machine learning for Earth observation, with a decade of experience across aerospace and EO startups. He builds physics-informed algorithms and uncertainty-aware models for atmospheric and emissivity retrievals, super-resolution, and wildfire spread forecasting, translating research into operational products at OroraTech. His background spans embedded and safety-critical C++ development for robotics and satellite payloads to neural-network deployment for vision and system identification at NASA Ames, giving him a rare blend of flight-proven engineering and ML research. He has collaborated with ESA’s Φ-lab and contributed to projects that require both rigorous physical modelling and practical productionization. Based in Munich, he combines aerospace training from the University of Stuttgart with hands-on experience integrating sensors, software, and models for high-stakes remote sensing applications. He is particularly skilled at bringing uncertainty quantification into operational pipelines, making predictive outputs more trustworthy for decision-making.
9 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Luft- und Raumfahrttechnik, Aeronautik und Astronautik, Master of Science - MS, Luft- und Raumfahrttechnik, Aeronautik und Astronautik at University of Stuttgart
Contributions:10 PRs, 27 pushes, 1 branch in 1 year 11 months
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