The School of Physical and Mathematical Sciences at Nanyang Technological University hosts the Division of Mathematical Sciences, which combines strength in pure and applied mathematics, statistics, and mathematical data science, with an active programme at the interface of dynamical systems, machine learning, and quantum information. The position sits in the GOAL group (GeOmetry, dynAmics, and Learning), led by Prof. Juan-Pablo Ortega.

We are looking for a Senior Research Fellow to contribute to the theoretical foundations of quantum reservoir computing within a JST–A*STAR joint project carried out with Prof. Kohei Nakajima's group at the University of Tokyo and collaborators in Thailand, Switzerland, and Spain. The role focuses on the mathematical analysis of input-driven open quantum dynamics: memory and echo state properties, learnability and generalization for temporal quantum learning, stochastic universality, and quantum reservoir-induced kernels. The fellow will act as the main scientific link between the NTU and Tokyo teams, with research stays at the University of Tokyo.

Key Responsibilities:

• Analyse memory and echo state properties of quantum reservoirs, including non-Markovian and infinite-dimensional settings.
• Extend stochastic state-space and state-affine frameworks to quantum reservoirs with measurement-induced randomness.
• 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, present at international conferences, and support project workshops and the associated international conference.
• Coordinate with the Japan-based team and collaborators, and co-supervise a graduate student.

Job Requirements:

• PhD in mathematics, applied mathematics, physics, control theory, electrical engineering, or a related field with 4 years of relevant experience.
• Research record in open quantum systems, quantum control, quantum stochastic processes, quantum information, or the mathematics of dynamical systems and machine learning.
• Knowledge of input-driven and stochastic dynamical systems, operator theory, or functional analysis; familiarity with kernel/RKHS methods or statistical learning theory is a strong advantage.
• Publications in leading journals and the ability to produce original theoretical results.
• Proficiency in Python, MATLAB, or Julia for numerical work.
• Excellent communication skills, ability to work independently and in international collaborations, and willingness to travel for research stays.

We regret to inform that only shortlisted candidates will be notified.