Postdoctoral positions are available in the research group led by Muhammad Ismail at Universiti Malaysia Sabah, Malaysia. The group is seeking 1–2 postdocs to contribute to cutting-edge research in deep reinforcement learning, traffic modeling and control, and neural combinatorial optimization. The overarching mission is to leverage artificial intelligence and machine learning to address challenging optimization problems and enable evidence-driven decisions in real-world socio-technical systems.

Research areas include deep reinforcement learning, traffic modeling and control, neural combinatorial optimization, and the application of AI/ML techniques to complex optimization scenarios. The group is particularly interested in candidates with expertise in machine learning, optimization, and the development of innovative solutions for socio-technical systems.

Applicants should hold a PhD in computer science, mathematics, engineering, or a related discipline. Preferred qualifications include a strong background in deep reinforcement learning, traffic modeling, neural combinatorial optimization, or artificial intelligence. Experience with machine learning, optimization, and evidence-driven decision-making is highly valued. Excellent research and communication skills are essential.

Applications are reviewed on a rolling basis, with positions available immediately. Interested candidates should visit the provided application link for further details and instructions on how to apply. The group encourages applications from individuals with diverse backgrounds and a passion for advancing research in AI and optimization.

For more information about the group and research topics, please refer to the supervisor's LinkedIn profile and the application link. Funding details are not specified in the announcement; candidates are encouraged to inquire directly regarding salary, benefits, and funding structure.