Digital Twin & Surrogate Modelling Intern M/F

À propos du poste

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!

• 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 A candidate with an interest in scientific software who can structure an open-ended problem and make progress autonomously while keeping the team informed.

• 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 • 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