Paris, France
Digital Twin & Surrogate Modelling Intern
Alice & Bob · Paris · France
Posted: 19/10/2026 · Last checked: 20/10/2026
Job description
Quoted in full from Alice & Bob's job posting. Gavroche does not edit it.
Alice & Bob is developing the first universal, fault-tolerant quantum computer to solve the world’s hardest problems.
The quantum computer we envision building is based on a new kind of superconducting qubit: the Schrödinger cat qubit 🐈⬛. In comparison to other superconducting platforms, cat qubits have the astonishing ability to implement quantum error correction autonomously!
We're a diverse team of 250+ brilliant minds from over 35 countries united by a single goal: to revolutionise computing with a practical fault-tolerant quantum machine. Are you ready to take on unprecedented challenges and contribute to revolutionising technology? Join us, and let's shape the future of quantum computing together!
Alice & Bob is developing the first universal, fault-tolerant quantum computer to solve the world’s hardest problems.
The quantum computer we envision building is based on a new kind of superconducting qubit: the Schrödinger cat qubit 🐈⬛. In comparison to other superconducting platforms, cat qubits have the astonishing ability to implement quantum error correction autonomously!
We're a diverse team of 250+ brilliant minds from over 35 countries united by a single goal: to revolutionise computing with a practical fault-tolerant quantum machine. Are you ready to take on unprecedented challenges and contribute to revolutionising technology? Join us, and let's shape the future of quantum computing together!
PURPOSE OF THE ROLE
The intern will investigate whether high-fidelity simulations, fast surrogate models and experimental data can be combined into a digital twin of Alice & Bob's cat-qubit devices. As these devices scale up, the predictive accuracy of physical and numerical models becomes harder to guarantee, and measurements from chips in our cryostats can validate and refine the models.
Data assimilation is the general framework that combines simulations and experimental data: it updates model states from measurements, accounting for model and measurement uncertainties, so the models stay accurate as new observations arrive. High-fidelity simulations, however, are costly, which limits how many the team can run. Surrogate and multi-fidelity models ease this limit by delivering high-fidelity results at a fraction of the cost, and they integrate into data assimilation. Faster and more accurate simulations then let the team explore more scenarios within the same compute budget.
The intern will build a proof of concept on a representative use case, defined jointly with the team during the first weeks. The deliverables are a validated prototype and the methodological and technical building blocks of a future digital-twin framework. Reporting to the Scientific Computational Engineer, the intern will work with the Simulation Engine team and with the teams producing experimental data.
The intern will investigate whether high-fidelity simulations, fast surrogate models and experimental data can be combined into a digital twin of Alice & Bob's cat-qubit devices. As these devices scale up, the predictive accuracy of physical and numerical models becomes harder to guarantee, and measurements from chips in our cryostats can validate and refine the models.
Data assimilation is the general framework that combines simulations and experimental data: it updates model states from measurements, accounting for model and measurement uncertainties, so the models stay accurate as new observations arrive. High-fidelity simulations, however, are costly, which limits how many the team can run. Surrogate and multi-fidelity models ease this limit by delivering high-fidelity results at a fraction of the cost, and they integrate into data assimilation. Faster and more accurate simulations then let the team explore more scenarios within the same compute budget.
The intern will build a proof of concept on a representative use case, defined jointly with the team during the first weeks. The deliverables are a validated prototype and the methodological and technical building blocks of a future digital-twin framework. Reporting to the Scientific Computational Engineer, the intern will work with the Simulation Engine team and with the teams producing experimental data.
This internship will start in February 2027 for a duration of 6 months!
Responsibilities:
- Develop and apply surrogate models
- Build fast approximations of high-fidelity simulations
- Assess their accuracy, uncertainty and domain of validity - Combine models and experimental data
- Implement data assimilation methods linking measurements to simulations
- Quantify model and measurement uncertainties - Run and analyse simulations
- Use the team's high-fidelity simulation tools to generate and interpret results
- Compare simulation outputs with experimental data - Develop scientific software
- Write clean, tested and documented code (Python / JAX) within the team's codebase
- Contribute to the design of reusable components for a digital-twin framework - Communicate and collaborate
- Present results and methodological choices to the team
- Work with experimental and design teams to understand data and needs
- Develop and apply surrogate models
Requirements:
- EDUCATION: Final year of a Master's degree (M2) or engineering school in physics, applied mathematics or scientific computing
- LANGUAGES: Proficient in English
- EXPERIENCE: No professional experience required, since coursework and research projects can supply the skills above. A previous internship or research project in scientific computing or numerical simulation is appreciated.
- Background in engineering
- Solid foundations in numerical analysis and numerical methods
- Knowledge of data assimilation methods, including uncertainty quantification
- Knowledge of reduced-order modelling
- Experience with open-source scientific simulation software
- Proficiency in Python, with strong experience in JAX
- Good software engineering practices, namely version control (Git), testing, documentation, CI/CD, and verification & validation (V&V)
- Rigour, autonomy and curiosity, and the ability to explain technical choices clearly
A candidate with an interest in scientific software who can structure an open-ended problem and make progress autonomously while keeping the team informed.
Nice to have:
- Statistics or Bayesian inference
- Experience with PyTorch or TensorFlow
- Deep learning
- Quantum physics
- Circuit engineering
- Rigorous use of agentic workflows for scientific research and implementation
Benefits:
- 1 day off per month
- Half of transportation cost coverage (as per French law)
- Meal vouchers with Swile, as well as access to a fully equipped and regularly stocked kitchen
Research shows that women might feel hesitant to apply for this job if they don't match 100% of the job requirements listed. This list is a guide, and we'd love to receive your application even if you think you're only a partial match. We are looking to build teams that innovate, not just tick boxes on a job spec.
You will join of one of the most innovative startups in France at an early stage, to be part of a passionate and friendly team on its mission to build the first universal quantum computer!
We love to share and learn from one another, so you will be certain to innovate, develop new ideas, and have the space to grow.
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