Grenoble, Auvergne-Rhône-Alpes, France
PhD-trained data scientist and Team Leader driving AI innovation in semiconductor manufacturing at Pollen. I lead the Application & Support team, developing deep-learning solutions for automated metrology and defect detection in semiconductor production. My work bridges cutting-edge research with real-world impact—helping manufacturers accelerate R&D, optimize yields, and transform process control. Core expertise: • AI/ML for semiconductor metrology and defect inspection • SEM/TEM image analysis and automation • Materials science and electronic structure simulation • Customer-facing technical leadership With 50+ scientific publications and deep roots in theoretical physics, I'm passionate about translating complex technology into practical solutions that solve manufacturing challenges. Let's connect if you're exploring how collaborative AI can revolutionize semiconductor processes.
- Leading smartdef project for deep-learning based defect detection solutions. - Assessing customer needs and providing continuous support. - Developing algorithms for Metrology analysis.
- Electronic transport calculations in 2D materials-based structures - Multi-scale modeling of materials heterostructures. - Writing research articles and scientific reports - Languages / technologies used : Python, Fortran, Bash script, Linux, HPC
-Assist in understanding and optimizing the physical mechanisms behind electric-field control on intrinsic magnetic properties. - Modeling Heusler compounds for future magnetic memory and logic devices. - Performed teaching at undergraduate levels. - Writing research articles and scientific reports - Developed national and worldwide collaboration - Languages / technologies used : Python, Fortran, Bash script, Linux, HPC
- Estimation of carbon nano cages surface reaction and topological order of graphene nonporous. - Coordinate and collecting results for scientific presentation. - Languages / technologies used: Fortran, Bash script, Linux, HPC
- Development of innovative solutions in response to physical problems related to memories applications. - Coordination of common tasks between several European teams that are part of the research project (graphene flagship). - Optimization of proximity effects between graphene and magnetic insulators. - Quantifying microscopic origin of magnetic anisotropy in magnetic tunnel junctions. - Writing research articles and scientific reports - Languages / technologies used: Fortran, Python, Linux, HPC