M/F - PhD Position « Smart growth of thermoelectric oxides by epitaxy assisted with operando monitoring and machine learning”

Job Description

Thesis Subject

This PhD project will be carried out at the Institut des Nanotechnologies de Lyon (INL) within the “Functional Materials and Nanostructures” team, within the framework of the funded PEPR DIADEM CINEMA project (2026-2030). The research focuses on the development of smart growth strategies for functional oxide thin films using molecular beam epitaxy (MBE) combined with in situ monitoring and machine learning approaches. Optimizing the relationship between growth conditions and material properties in functional materials still largely relies on empirical approaches such as trial-and-error or design of experiments. These methods are time and resource consuming and require highly stable processes that are rarely achieved in practice. This project proposes to develop a new methodology for smart growth control applied to p-type BaSnO₃, a wide-bandgap perovskite oxide of growing interest for transparent electronics and thermoelectric applications, based on the combined use of advanced in situ characterization tools and machine learning-driven data analysis. The oxide MBE chamber at INL is equipped with several real-time monitoring techniques including reflection high-energy electron diffraction (RHEED), spectroscopic ellipsometry, wafer curvature measurements, and optical flux monitoring. These tools provide complementary information about surface structure, growth dynamics, optical properties, strain, and stoichiometry during thin film deposition. The objective of the PhD thesis is to exploit these measurements, combined with machine learning approach, to develop data-driven models capable of correlating the growth parameter and functional properties of p-type BaSnO₃. BaSnO₃ is a perovskite oxide known for its exceptionally high electron mobility in its n-type form (La-doped), but its p-type counterpart remains largely unexplored and poorly controlled, making it a highly relevant and challenging target for this project.

The PhD thesis combines data-driven analysis and materials research. The objective will be to develop two complementary approaches for growth optimization. The first approach is based on coupled operando measurements, combining real-time curvature monitoring, RHEED, and spectroscopic ellipsometry to directly probe the structural and optical state of the growing film in order a deep understanding of the growth physics and the truly impact of elaboration parameters on materials properties. The second approach relies on supervised machine learning (ML) and Baysian Optimisation (BO) algorithms to establish correlations between the key elaboration parameters (Ba flux, Sn flux, dopant flux, O₂ pressure, substrate temperature) and a functional material property such as the electrical resistivity. These methods are particularly well suited to small experimental datasets, as typically encountered in MBE. To train and validate these models, the database will first be built using n-type BaSnO₃ data extracted from the literature, a well-documented system that serves as an ideal benchmark. The PhD student will then construct their own experimental database from their own growth runs of p-type BaSnO₃, progressively enriching the model with original data. Once this methodology is validated on the n-type system, the final objective will be to apply both approaches — operando control and ML-guided optimization — to the growth of in situ measurements and establish correlations between growth conditions and material properties. By combining operando monitoring, machine learning and ex situ characterization, the project aims to establish predictive models linking growth parameters, defect chemistry, and transport properties, with the ultimate goal of achieving high-performance p-type BaSnO₃ with optimized resistivity and carrier concentration beyond the current state of the art.

• Master degree (M2 or equivalent) in physics, materials science or a related field preferably with a master thesis related to IA data treatment
• Interest in experimental materials science and thin-film growth
• Knowledge in machine learning or data analysis applied to materials science
• Experience in thin film growth and characterization
• Motivation to work with advanced characterization and growth techniques
• The candidate should be able to work independently while collaborating effectively with the research team.
• Good communication skills and fluency in English are required.

Your Work Environment

The Institut des Nanotechnologies de Lyon (INL) aims to develop multidisciplinary technological research in the field of micro and nanotechnologies and their applications. The research carried out ranges from materials to systems. The laboratory is supported by Lyon's NanoLyon technology platform.
The areas of application cover major economic sectors: the semiconductor industry, information technologies, life and health technologies, energy and the environment.
The laboratory is multi-site, with locations on the Ecully and Lyon-Tech La Doua campuses. It employs around 240 people, including 121 permanent staff. The INL is a major player in the Research and Teaching Cluster.
This position is located in an innovative environment, at the cutting edge of future technologies, in strategic application sectors.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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