Senior Research Fellow (Quantum Reservoir Computing and Quantum Machine Learning)
Core
Theoretical analysis of input-driven open quantum dynamics, focusing on memory, echo state properties, learnability, and generalization for temporal quantum learning within a JST–A*STAR joint project.
Role type
Senior Research Fellow (Quantum Reservoir Computing and Quantum Machine Learning)
Builds
Theoretical frameworks and numerical benchmarks for quantum reservoir computing
Domain
Quantum information, dynamical systems, and machine learning
Deliverable
research
Required skills
Mathematical analysis of open quantum systems, operator theory, functional analysis, stochastic dynamical systems, kernel/RKHS methods, statistical learning theory
Preferred skills
Python, MATLAB, or Julia for numerical work
Technologies
Python, MATLAB, Julia
Responsibilities
Analyse memory and echo state properties of quantum reservoirs in non-Markovian and infinite-dimensional settings; Extend stochastic state-space and state-affine frameworks to quantum reservoirs; Derive learnability and generalization guarantees for temporal quantum learning; Construct and analyse quantum reservoir-induced kernels and associated finite-sample bounds; Run numerical simulations and benchmarks validating the theory; Publish in leading journals and present at international conferences; Coordinate with the Japan-based team and co-supervise a graduate student
Seniority
Senior, hands-on IC with research leadership
