The posting, in Alice & Bob's own words
archived Oct 19, 2026Alice & 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
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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, , and verification & validation (V&V) Rigour, autonomy and curiosity, and the ability to explain technical choices clearly