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# Internship - Superconducting Electronics For Quantum Computing Applications - Grenoble M - F H

- **Company:** CEA Industrie
- **Location:** Grenoble, France
- **Experience:** Internship
- **Contract:** Internship / Graduate position
- **Skills:** Python (Programming Language), LTspice, MATLAB, Quantum Computing, Signal Processing, Verilog, VHSIC Hardware Description Language (VHDL)
- **Published:** October 28, 2026
- **Apply:** https://www.hellowork.com/fr-fr/emplois/84904031.html

## About the Role

We are looking for a candidate with an engineering degree in physics, electronics, or microelectronics, or a master's degree in applied physics, quantum electronics, or nanotechnologies. Prior experience in circuit design (analog, digital, or RF) will be a major asset. Desired Technical Skills :Experience in data processing and numerical analysis (Python, MATLAB, or similar tools).Strong foundation in applied mathematics (signal processing, physical modeling), with knowledge of quantum computing being appreciated.Familiarity with circuit simulation tools (SPICE, LTspice, Cadence, or equivalents).Knowledge of hardware description languages (HDL: Verilog, VHDL) for modeling digital systems.Having studied superconductivity and/or the Josephson effect is a plus.Prior experience in a cryogenic environment (low-temperature handling, device characterization) would be a true asset. Personal Qualities :Scientific curiosity and a taste for applied research.Autonomy
and the ability to synthesize complex information (literature review, state-of-the-art analysis).Initiative to propose innovative solutions in an emerging field.Rigor and organizational skills to manage a project at the interface between theory and experimentation. This internship is aimed at candidates who are motivated by the technological challenges of quantum computing and wish to invest in a high-potential project, with the possibility of pursuing a PhD., Experience in data processing and numerical analysis (Python, MATLAB, or similar tools).
Strong foundation in applied mathematics (signal processing, physical modeling), with knowledge of quantum computing being appreciated.
Familiarity with circuit simulation tools (SPICE, LTspice, Cadence, or equivalents).
Knowledge of hardware description languages (HDL: Verilog, VHDL) for modeling digital systems.
Having studied superconductivity and/or the Josephson effect is a plus.
Prior experience in a cryogenic environment (low-temperature handling, device characterization) would be a true asset., Scientific curiosity and a taste for applied research.
Autonomy and the ability to synthesize complex information (literature review, state-of-the-art analysis).
Initiative to propose innovative solutions in an emerging field.
Rigor and organizational skills to manage a project at the interface between theory and experimentation.

This internship is aimed at candidates who are motivated by the technological challenges of quantum computing and wish to invest in a high-potential project, with the possibility of pursuing a PhD.

## Description

Technological Review: Conduct a state-of-the-art review of superconducting electronics technologies (SFQ, AQFP, eSFQ, etc.), analyzing their specificities, performances, and potential for quantum computing applications.

 - Functional Analysis: Identify and evaluate the key functions of the hardware stack of QPUs (spin qubits and superconducting qubits) for which SFQ blocks or other superconducting technologies could provide innovative solutions, particularly in terms of performance, energy consumption, and cryogenic integration.
 - Circuit Design and Validation:

 - Implement state-of-the-art superconducting circuits in the JoSim simulation environment.
 - Participate in setting up a complete design flow: schematic capture, layout, parasitic extraction, and results analysis.
 - Validate performance hypotheses and highlight these results in the context of quantum computing.
 - Architectural Perspectives : Contribute to the identification of different architectural approaches and key technological building blocks to address the challenges of scaling QPUs.