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

The Centre for Quantum Technologies at NTU invites applications for a Research Fellow position in Quantum-Enhanced Learning, Agents and Algorithms. The successful candidate will conduct independent and collaborative research at the intersection of quantum information science, machine learning, statistical learning theory, quantum thermodynamics, and complexity science.

The Research Fellow will develop novel theoretical frameworks and algorithms for quantum-enhanced learning and intelligent agents, with research directions including quantum advantages in data-sparse learning, quantum algorithms for learning and sampling, temporal quantum information processing, non-Markovian dynamics, and quantum resource theories. The role also involves contributing to interdisciplinary research initiatives, publishing high-impact scientific findings, preparing research proposals, and presenting research outcomes at conferences and seminars.

The successful candidate will work closely with faculty members, researchers, and students within the international research network, while supporting the supervision and mentoring of postgraduate students. This position offers an opportunity to contribute to cutting-edge research with both fundamental scientific significance and potential real-world applications.

Key Responsibilities

• Conduct high-quality research in quantum-enhanced learning, quantum information science, and related areas.

• Develop new theoretical models, methodologies, and algorithms.

• Publish research outcomes in leading peer-reviewed journals and conferences.

• Prepare grant applications, technical reports, and research presentations.

• Collaborate with internal and external researchers across multiple disciplines.

• Contribute to the supervision and development of graduate students.

• Support the achievement of research milestones and project objectives.

• PhD in Quantum Information, Theoretical Physics, Computer Science, Mathematics, Machine Learning, or a closely related discipline. Candidates nearing completion of their PhD may also be considered.

• Demonstrated research excellence through publications, conference presentations, or related scholarly achievements.

• Quantum algorithms

• Quantum stochastic processes and quantum combs

• Machine learning and statistical learning theory

• Reinforcement learning

• Differential privacy

• Model reduction and distillation

• Quantum reservoir computing

• Variational quantum circuits

• Quantum resource theories and quantum thermodynamics

• Strong analytical, problem-solving, and quantitative research skills.

• Excellent written and verbal communication skills.

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

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