Digital Twin & Surrogate Modelling Intern M/F

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! 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 Requirements: 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 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