Research Fellow I

SUTD is the world’s first Design AI university. With Design AI, artificial intelligence is treated as a partner and a member of the team – not just a tool. As a result of this unique SUTD treatment, AI and humans brainstorm, spar and prototype together, resulting in solutions that are elevated several-fold. This human-AI team concept has been made possible because of SUTD’s unique cohort-based interdisciplinary pedagogy. As a trailblazer in the field of design and technology education and research, SUTD has been pioneering innovative programmes and initiatives since its formation 16 years ago. As part its new mid-term growth strategy called SUTD Leap, it will work closely with industry to co-create solutions in both education and research – for a better world.

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.