Research Fellow (Quantum Machine Learning)

National University of Singapore; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

National University of Singapore invites excellent candidates for a Research Fellow in Quantum Machine Learning to join a joint effort between the MathEXLab (Mechanical Engineering) and the Centre for Quantum Technologies. The project leverages leading experts and advanced quantum computing infrastructure.

The successful candidate will work within a multidisciplinary team that designs classical and quantum algorithms, implements software, and applies results to drug discovery and molecular

Qualifications

• Experience in machine learning for research projects.
• Focus on quantum machine learning methods and algorithms.
• Background in dynamical systems and simulation approaches.
• Familiarity with algorithms for simulating classical dynamics including climate-related targets.

Responsibilities

• Collaborate on multidisciplinary design of quantum and classical ML algorithms.
• Develop production-grade software implementations for research outcomes.
• Contribute to applications in drug discovery and molecular modelling.

Skills

Machine learning

Quantum ML

Dynamical Systems

Algorithms

Job description

Job Title: Research Fellow (Quantum Machine Learning)

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Mechanical Engineering

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