Our client develops autonomous machining and inspection systems for high-precision manufacturing, serving industries such as aerospace, MedTech, and high-end mechanical engineering. Their technology combines robotics, optics, and advanced sensing to enable precise, data-driven quality control of complex manufactured components.

Responsabilities:

• Develop mathematical models and algorithms for high-precision optical measurement methods, translating physical and optical phenomena into robust, quantifiable models.
• Investigate how machine, sensor, and process parameters influence measurement accuracy, using a hypothesis-driven approach combining experimentation, data analysis, and validation.
• Quantify design trade-offs between accuracy, resolution, cycle time, and robustness, and translate these insights into concrete system specifications.
• Develop and evaluate statistical inference and sensor fusion approaches in Python to quantify, model, and actively control measurement uncertainty across the system.

Profile:

• A university degree (Bachelor's, Master's, or PhD) in mathematics, statistics, physics, or another comparable quantitative field.
• Strong knowledge of mathematical modelling, statistics, and numerical methods, with the ability to apply them to real-world engineering problems.
• A solid understanding of the interface between hardware and software: you know how mechanics, electronics, and optics behave in a real machine, and you can translate that physical behaviour into mathematical models and algorithms.
• Confident, hands-on use of Python as a scientific working tool (e.g., NumPy, SciPy) for data analysis, modelling, and simulation.
• Strong communication skills and genuine enjoyment of close, cross-disciplinary teamwork, with good spoken and written English or French.
• An independent, research-oriented working style, intellectual curiosity, and enthusiasm for experimental research and quantitative problem-solving.