Thèse – Optimisation IA de l’épitaxie 2D h‑BN

About the role

This PhD thesis focuses on improving the epitaxial quality of hexagonal boron nitride (h‑BN) and its layered heterostructures using artificial intelligence. The research is part of the PEPR DIADEM program funded by the French National Research Agency and will be conducted in the joint IRL 2958 GT‑CNRS laboratory.

Key responsibilities

• Develop AI‑driven models to optimise MOVPE growth parameters for h‑BN.
• Perform metal‑organic vapor phase epitaxy (MOVPE) of h‑BN based heterostructures.
• Carry out advanced material characterisation (XRD, AFM, SEM, PL, CL, electrical measurements).
• Analyse experimental data to refine growth models.
• Collaborate with national research partners and contribute to project reports.

Required profile

• Master’s degree in Materials Science, Physics, Electrical Engineering or a related field.
• Strong interest in semiconductor materials and AI‑based optimisation.
• Ability to work in a collaborative, interdisciplinary environment.

Required skills

• Artificial Intelligence / Machine Learning for process optimisation.
• Metal‑Organic Vapor Phase Epitaxy (MOVPE) techniques.
• X‑ray Diffraction (XRD).
• Atomic Force Microscopy (AFM).
• Scanning Electron Microscopy (SEM).
• Photoluminescence (PL) and Cathodoluminescence (CL) spectroscopy.
• Electrical measurement methods.