Portal > Offres > Offre UMR7647-KAROUA-002 - Post-doctorat (H/F) ANR GASPE: Croissance plasma de semi-conducteurs à large bande interdite : diagnostic Térahertz et optimisation par intelligence artificielle

Post-doctoral position (M/F) ANR GASPE: Terahertz diagnostic and AI-driven optimization of plasma grown ultra-wide bandgap semiconductors

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- Français-- Anglais
Application Deadline : 10 November 2025 23:59:00 Paris time

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General information

Offer title : Post-doctoral position (M/F) ANR GASPE: Terahertz diagnostic and AI-driven optimization of plasma grown ultra-wide bandgap semiconductors (H/F)
Reference : UMR7647-KAROUA-002
Number of position : 1
Workplace : PALAISEAU
Date of publication : 20 October 2025
Type of Contract : Researcher in FTC
Contract Period : 12 months
Expected date of employment : 5 January 2026
Proportion of work : Full Time
Remuneration : 3081,33 to 3519, 85€
Desired level of education : Doctorate
Experience required : 1 to 4 years
Section(s) CN : 10 - Fluid and reactive environments: transport, transfer, transformation processes

Missions

We are seeking a highly motivated postdoctoral researcher to join our team at LPICM, a joint CNRS–École Polytechnique laboratory, to work on an innovative project combining advanced diagnostics and artificial intelligence for the synthesis of ultra-wide bandgap (UWBG) semiconductors. This project aims at developing new understanding and control of plasma-assisted deposition processes for materials such as GaN, AlN, and Ga₂O₃ grown at low temperature.

Activities

The postdoc will focus on coupling advanced in-situ diagnostic tools — in particular Terahertz Spectroscopy — to probe both plasma and films. These diagnostics will be used to extract plasma parameters such as electron density and molecules density, and materials paramaters, offering a unique window into the early stages of film nucleation and growth.
In parallel, the postdoctoral researcher will perform deposition/characterization and develop AI-based frameworks for data analysis and process optimization. Machine learning and deep learning models will be trained to correlate plasma parameters, diagnostics outputs, and material properties, enabling predictive control of growth conditions for achieving optimal film quality. This dual approach, combining physics-driven diagnostics and data-driven modeling, will establish a new paradigm in intelligent thin-film processing.

Skills

Applicant should hold a PhD in plasma physics, materials science, or a related field, with experience in plasma-based thin film deposition (PVD, CVD, sputtering, etc.). Hands-on knowledge of spectroscopic diagnostics (THz spectroscopy, ellipsometry, emission spectroscopy, etc.) is highly desirable.
Candidate with interest or background in artificial intelligence, machine learning, or data-driven process modeling are particularly encouraged to apply. Experience in signal processing, or big-data experimental workflows will be an asset.
The ideal candidate is autonomous, curious, and motivated by interdisciplinary research combining experimental plasma physics, materials science, and computational intelligence.

Work Context

The candidate will have access to cutting-edge facilities at LPICM and the École Polytechnique.