News : FMNT

October 25 2024

New approach to develop selective surfaces for biosensors. Application to life sciences.

New approach to develop selective surfaces for biosensors. Application to life sciences.     Keywords: Biosensor, Sensitive surface, Functionalization, Microelectronics, Optics, Nano, simulation  and characterization of components. Location: CROMA laboratory (UMR 5130). Supervisors: Olivier Lavastre, Edwige Bano (CROMA) and Valérie Stambouli (LMGP). Period and duration: Spring 2025, 6 months (for M2) or 3 months (for […] >>

October 15 2024

Study of selective contacts and p-type connection layers based on indium-free transparent conductive oxides for 2T tandem cells

      Study of selective contacts and p-type connection layers based on indium-free transparent  conductive oxides for 2T tandem cells For the development of new efficient p-type TCO thin films as key step toward transparent electronic, the internship focuses on the development of a p-type oxide thin film material for solar applications. Context : […] >>

October 04 2024

Measuring protein adsorption on surfaces with integrated optical sensors

   Master 2 thesis/ PFE  –  5 to 6 months            Measuring protein adsorption on surfaces with integrated optical sensors The adsorption of proteins on surfaces plays an important role in the biomedical field [1]. For diagnostics, the control of the adsorption of immobilized capture antibodies is crucial for accurate immuno-assays. Because of their tendency […] >>

October 04 2024

Spintronics in graphene over transition metal dichalcogenides: Simulations

MASTER 2 – Duration: 6 months  -Start period: February/ March 2025 Possibility of PhD thesis Context : In the context of spintronics, graphene is considered an ideal platform thanks to its very weak spin-orbit coupling (SOC), which allows spin scattering lengths of up to a few tens of micrometers [1]. For the same reason, however, […] >>

October 03 2024

Edge-AI Solutions for Real-Time Hyperfrequency Material Characterization

  HyperAI: Edge-AI Solutions for Real-Time Hyperfrequency Material    Characterization    Context: The electrical characterization of materials at hyperfrequencies is essential for understanding their intrinsic electronic structure and charge carrier dynamics. Permittivity and dielectric losses are a major concern in this field, as they directly impact signal integrity and propagation within high-speed electronic systems. Due to […] >>
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