Research Fellow (Quantum Machine Learning)

National University of Singapore; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

National University of Singapore invites applications for a Research Fellow (Quantum Machine Learning) in the College of Design and Engineering, Mechanical Engineering. The position focuses on integrating classical ML with quantum approaches to advance simulations in drug discovery and molecular modelling.

The successful candidate will join a multidisciplinary team developing both quantum and classical algorithms, with robust production-grade software implementations and strong collaboration

Qualifications

• Proficient in machine learning methods and frameworks.
• Experience with quantum machine learning concepts.
• Understanding of dynamical systems theory and simulations.
• Knowledge of algorithms for simulating classical dynamical systems (e.g., transport, multiscale flows).
• Experience with signal processing and time-evolution methods.

Responsibilities

• Collaborate on integrating classical ML with quantum algorithms for research in drug discovery and molecular modelling.
• Develop and implement robust, production-grade software for quantum/classical workflow.
• Contribute to the design and evaluation of quantum and/or classical algorithms in multidisciplinary teams.

Skills

Machine learning

Quantum Machine Learning

Dynamical Systems

Algorithms for dynamical systems

Signal processing

Job description

Research Fellow (Quantum Machine Learning)

Job Title: Research Fellow (Quantum Machine Learning) University-Level Unit: College of Design and Engineering Faculty/Department-Level Unit: Mechanical Engineering Employee Category: Research Staff Location_ONB: Kent Ridge Campus Posting Start Date: 12/08/2026

Job Description

We invite excellent candidates in classical machine learning to join a collaborative research initiative between the MathEXLab of NUS (Mechanical Engineering) and the Centre for Quantum Technologies (CQT). The project benefits from access to both leading experts in the field and advanced quantum computing infrastructure.

The successful candidate will work within a multidisciplinary team that combines classical and quantum algorithm design, software implementation, and applications in drug discovery and molecular modelling. The research will contribute to the development of both quantum and/or classical algorithms, supported by robust, production-grade implementations.

Qualifications

• Machine learning
• Quantum Machine Learning
• Dynamical Systems
• Algorithms for the simulation of classical dynamical systems (including climate-relevant targets such as transport, multiscale flows, and extremes)
• Signal processing, time evolution, and structure-preserving transformations