Summary
Nicolas Winckler is an applied scientist and research engineer with 12 years of experience building and deploying ML and CV systems across industry and top research institutes. He combines a PhD in physics with deep practical expertise in modern C++ development, large-scale data pipelines, signal processing and state-of-the-art deep learning for anomaly detection, survival analysis, and object detection. At Ipsotek and Atos he led reproducible ML lifecycles, hyperparameter optimization practices and federated/privacy-preserving learning while co-supervising PhDs and contributing to academic and industry consortia. He has authored a Springer book and multiple publications and patents, and translated diffusion-model research into practical annotated datasets and guidelines for regulatory-ready computer vision projects. Now at Bull he provides scientific support across chatbot disambiguation, XAI/trustworthy AI and proactive maintenance for HPC, reflecting a rare blend of research rigor and production-grade engineering. An uncommon strength is his ability to bridge experimental physics-style analysis with scalable ML engineering, making complex models auditable and operational.
12 years of coding experience
5 years of employment as a software developer
Master, Physics, Master, Physics at Karlsruhe Institute of Technology (KIT)
Doctor of Philosophy (Ph.D.), Physics, Doctor of Philosophy (Ph.D.), Physics at Justus Liebig University Giessen
Master, Physics, Master, Physics at Université Joseph Fourier (Grenoble I)
French, English, German