Research Fellow I

SUTD (Singapore University of Technology & Design); Singapore; On-site; SGD 60,000 - 100,000; Full time

Job summary

SUTD invites applications for a Research Fellow to advance optimization, machine learning, and game theory. You will develop novel algorithms for large-scale decision-making and collaborate with an interdisciplinary team at the Singapore University of Technology and Design.

You will supervise graduate students, contribute to proposals, publish in top venues, and engage with industry partners.

Qualifications

• PhD in a relevant field required.
• Strong publication record in international venues.

Responsibilities

• Conduct high-quality research in optimization, ML, and game theory, leading to publications.

Skills

Optimization

Machine Learning

Game Theory

Research

Education

PhD in Optimization/OR/Math/CS/EE/IE

Tools

Python

MATLAB

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.