Research internship: Quantum computing and AI for partial differential equations 2027 is a role within the Algorithms and Applications for PDEs team. The intern will contribute to the research agenda by developing algorithms and models, performing experimentation and validation, implementing quantum solvers, training AI models, or creating cross-platform workflows. This work addresses the challenge of solving forward and inverse problems for partial differential equations where traditional scaling limits are encountered. The role requires a working understanding of partial differential equations, elementary statistics, and proficiency in Python using NumPy. Preferred qualifications include research experience in applied mathematics, quantum computing, or AI models for PDEs. Specific technical expertise may include data assimilation methods, neural operators, Hamiltonian simulation, QSP/SVT, and various discretization methods like FEM or FDM to bridge the gap between scientific computing and emerging technologies.