Research Fellow (Quantum-Enhanced Learning, Agents and Algorithms)

The Centre for Quantum Technologies (CQT) at Nanyang Technological University (NTU) invites applications for a Research Fellow position in the area of Quantum-Enhanced Learning, Agents and Algorithms.

The successful candidate will conduct cutting-edge research at the intersection of quantum information science, machine learning, statistical learning theory, complexity science, and quantum thermodynamics. This role offers the opportunity to contribute to the development of novel quantum algorithms, learning frameworks, and intelligent quantum systems within a highly collaborative and internationally recognised research environment.

Key Responsibilities

• Conduct independent and collaborative research in quantum information science, machine learning, and related fields.

• Develop novel theoretical frameworks, algorithms, and models for quantum-enhanced learning and intelligent agents.

• Publish research outcomes in reputable peer-reviewed journals and present findings at scientific conferences.

• Contribute to the preparation of research proposals, grant applications, and technical reports.

• Collaborate with researchers from diverse disciplinary backgrounds to advance research objectives.

• Support the supervision and mentoring of postgraduate students where appropriate.

• Participate in the Centre's research activities, seminars, and academic initiatives.

Job Requirements

• PhD in Quantum Information, Theoretical Physics, Computer Science, Mathematics, Machine Learning, or a closely related discipline.

• Demonstrated research experience with a strong publication record in relevant fields.

• Strong analytical, mathematical, and problem-solving capabilities.

• Excellent written and verbal communication skills.

• Ability to work effectively both independently and within multidisciplinary research teams.

The following experience would be advantageous:

• Quantum algorithms and quantum computation.

• Machine learning, statistical learning theory, or reinforcement learning.

• Quantum information theory and quantum stochastic processes.

• Quantum resource theories and quantum thermodynamics.

• Variational quantum algorithms, quantum machine learning, or complexity science.

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