Research Fellow I

Singapore University of Technology and Design; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Singapore University of Technology and Design is seeking a Research Fellow to advance theory and methods at the intersection of optimization, machine learning, and game theory. You will develop algorithms for decision-making, collaborate with an interdisciplinary team, and contribute to high-impact publications.

The role involves supervising graduate students, engaging with academic and industry partners, and shaping research proposals and project directions.

Qualifications

• PhD in Optimization, Operations Research, Applied Mathematics, Computer Science, Electrical Engineering, Industrial Engineering, or a closely related discipline.
• Strong expertise in mathematical optimization, machine learning, or game theory, with demonstrated experience in optimization algorithms, statistical learning, reinforcement learning, multi‑agent systems, or decision‑making under uncertainty.
• Excellent research track record, including publications in high-quality international journals and conferences.
• Strong analytical, programming, and communication skills, with the ability to work both independently and collaboratively in an interdisciplinary environment.

Responsibilities

• Conduct high-quality research in optimization, machine learning, and game theory, leading to publications in top-tier journals and conferences.
• Develop novel optimization algorithms and learning methods for large-scale, stochastic, distributed, or multi-agent systems.
• Supervise and mentor graduate students and provide research guidance within the project team.
• Contribute to research proposals, project management, and collaborations with academic and industry partners.
• Disseminate research outcomes through publications, conference presentations, and engagement with the broader research community.

Skills

Mathematical optimization

Machine learning

Game theory

Optimization algorithms

Statistical learning

Reinforcement learning

Multi-agent systems

Decision-making under uncertainty

Analytical skills

Programming

Education

PhD in Optimization or related field

Job description

Job Title:

Research Fellow in Optimization, Machine Learning, and Game Theory

Job Summary:

We are seeking a highly motivated Research Fellow to conduct cutting‑edge research at the intersection of optimization, machine learning, and game theory. The successful candidate will develop novel theoretical, algorithmic, and computational methods for decision‑making, learning, and optimization, while collaborating with an interdisciplinary research team on both foundational and application‑driven research.

Key Responsibilities:

Conduct high-quality research in optimization, machine learning, and game theory, leading to publications in top-tier journals and conferences.

Develop novel optimization algorithms and learning methods for large-scale, stochastic, distributed, or multi‑agent systems.

Supervise and mentor graduate students and provide research guidance within the project team.

Contribute to research proposals, project management, and collaborations with academic and industry partners.

Disseminate research outcomes through publications, conference presentations, and engagement with the broader research community.

Requirements:

PhD in Optimization, Operations Research, Applied Mathematics, Computer Science, Electrical Engineering, Industrial Engineering, or a closely related discipline.

Strong expertise in mathematical optimization, machine learning, or game theory, with demonstrated experience in one or more of optimization algorithms, statistical learning, reinforcement learning, multi‑agent systems, or decision‑making under uncertainty.

Excellent research track record, including publications in high-quality international journals and conferences.

Strong analytical, programming, and communication skills, with the ability to work both independently and collaboratively in an interdisciplinary environment.