CENTRALE LYON - PhD Towards smart growth of functional oxides

Organisation/Company Ecole Centrale de Lyon Research Field Chemistry Computer science Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 May 2026 - 12:00 (Europe/Paris) Country France Type of Contract Permanent Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Sep 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Abstract

This PhD project aims at developing disruptive smart growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML) data analysis. Material developments will be supported by this new disruptive methodology.

Strengths and appeal of the subject

• Innovative research topic with strong scientific and technological innovation potential
• Team and laboratory environment: extensive technological resources, strong scientific expertise, and access to state-of-the-art characterization, growth, and nanofabrication facilities
• Broad and multidisciplinary topic, ranging from material growth to thermoelectric characterization, including instrumentation for smart growth control

External Collaborations / Partnerships

Partners of the PEPR DIADEM – CINEMA project funding this PhD: IRCER, Institut P’, ILM

Scientific Background and Motivation

Thermoelectric oxides are emerging as a sustainable and innovative solution. Unlike conventional materials, they are composed of abundant, low-cost, and non-toxic elements, while offering remarkable thermal and chemical stability. Among them, perovskite-structured oxides (ABO₃) stand out due to their high structural flexibility, which allows a wide variety of cation substitutions to finely tune their physical properties. Strontium titanate (SrTiO₃, STO), for instance, is a model system in terms of adaptability, capable of reaching thermoelectric power factors (PF = σS²) exceeding 40 µW cm⁻¹ K⁻² at room temperature — more than four times higher than that of (Bi,Sb)₂Te₃. Moreover, these materials can be grown as high-quality single-crystalline thin films, compatible with silicon- or gallium arsenide (GaAs)-based microelectronic platforms through techniques such as molecular beam epitaxy (MBE), a historical area of expertise at INL (Institut des Nanotechnologies de Lyon) (Figure 1).

PhD Objectives

Harnessing the thermoelectric potential of perovskite oxides requires extremely fine control over stoichiometry and crystalline structure, imposing growth control by MBE beyond the current state of the art. This level of control is made possible by the reactor developed at INL, which has been equipped with several real-time growth monitoring tools: RHEED (reflection high-energy electron diffraction), ellipsometry, wafer curvature measurements, and an optical flux measurement system. These tools are now fully operational and used routinely in the laboratory. first objective of the PhD will be to further develop this instrumentation by contributing (with the support of the team’s technical staff) to the implementation of an interface enabling real-time acquisition of signals from all characterization tools on a common time base. Based on this interface, in situ monitoring tools will be exploited to actively control the growth of perovskite oxides, by implementing Machine Learning data analysis to drive the growth conditions from the targeted/measured properties.

second objective will be to check and optimize the thermoelectric properties of already known ABO₃ structures, such as La-doped STO, from this disruptive methodology. Once this coupled approach has been mastered for these model materials, the ultimate goal will be to elaborate new p‑type perovskite ABO₃ structures with optimized thermoelectric properties, targeting a figure of merit beyond the state-of-the‑art. The properties will also be measured by the wide range of ex situ characterization tools available in the laboratory (X‑ray diffraction, XPD, AFM, electrical and transport measurements, etc.), and also from the team’s dense collaborative network.

Scientific Challenges

• Achieving coupled analysis of multiple in situ and real-time measurements using a common time base, in order to extract relevant physical parameters governing the properties of thermoelectric oxides and their dependence on growth conditions
• Achieving smart growth of thermoelectric oxides based on machine learning and IA-driven data analysis in real time from in situ characterization tools
• Achieving novel p‑type thermoelectric oxides with properties beyond the state‑of‑the‑art

References

The PhD candidate will be at the core of the project and will be particularly responsible for the following tasks:

• Growth of oxide‑based structures by molecular beam epitaxy and instrumental/software developments for the implementation of in situ and real‑time characterization tools.
• Structural and chemical characterization of epitaxial layers: X‑ray diffraction, XPS, AFM, transmission electron microscopy, and potential measurement campaigns at the SOLEIL and ESRF synchrotrons.
• Functional characterization of epitaxial layers, in particular electrical and transport measurements: Hall effect, Seebeck coefficient, etc.
• IA‑driven data analysis and smart growth implementation

For the first three activities, the PhD candidate will benefit from the broad range of expertise and facilities available within the laboratory and the team’s extensive collaborative network. The fourth activity will be supported by the expertise of the candidate and collaboration network (INL, ECL, IRCER, DIAMOND platform, CINTRA, …).

Supervision / Contacts

Clarisse Furgeaud (50%) , scientific expertise: epitaxy and magnetron sputtering, in situ and real‑time monitoring, structural and chemical characterization by TEM ( ‑lyon.fr)

Romain Bachelet (50%) , scientific expertise: epitaxy, materials science, energy materials, thermoelectric oxides ( ‑lyon.fr)

Candidate profile / Requirements

We are seeking a candidate holding a Master’s degree (M2 or equivalent), with skills in machine learning and IA‑driven data analysis, materials science, solid state physics/chemistry, motivated by experimental research and capable of synthesizing results from diverse characterization techniques. The project also requires strong collaboration and teamwork skills, as well as proficiency in scientific writing and oral communication.

The originality of the proposed research is expected to lead to potential patents, the publication of several articles in international peer‑reviewed journals, and to the participation of the PhD candidate in conferences and workshops.

Skills developed during the PhD

This PhD project will provide solid training for the candidate, including advanced skills in machine learning and IA‑driven data analysis for materials science, thin‑film growth, materials science, and mastery of state‑of‑the‑art structural and optical characterization tools. Upon completion of the PhD, the candidate will be fully familiar with the scientific research process and will have acquired the rigor, methodology, and technical expertise associated with it.

Career prospects after the PhD

Academic career or employment in the private sector.