Postdoctoral Associate (Resource Allocation in Human-machine Systems)

Project Overview

The "Mens, Manus and Machina—How AI Empowers People, Institutions and Cities in Singapore (M3S)" five-year project was initiated in July 2023. It is driven by the goal of investigating the nature of work, redefining our relationship with technology, and exploring how institutions can adapt to foster liveability, sustainability, innovation, and social welfare.

Successful applicants will have the opportunity to work on cutting-edge projects that aim to develop state-of-the-art AI to create future smart cities. This Postdoctoral Associate position is until June 2028 under the M3S program at SMART. The SMART team seeks to advance the frontier of AI research, apply it to society and cities, and demonstrate the concrete social impacts of the AI algorithms with broad public acceptance in Singapore.

Responsibilities

The SMART-T7 project concerns the design of human-machine systems for the scheduling and allocation of valuable resources in ways that accommodate and optimize for the needs and capabilities of both humans and machines. The project aims to solve diverse use cases in a broad set of application contexts.

The management of large-scale service and infrastructure systems—such as airport operations, urban mobility, last-mile logistics, robotic fleet management and other smart city applications—requires making tightly coupled scheduling and resource allocation decisions under significant operational complexity. These systems are characterized by high-dimensional, combinatorial decision spaces (e.g., assigning flights to gates, vehicles to multi-service tasks, lockers to dynamic demand, or robotic agents to coupled tasks), where interactions and coordination requirements between entities—such as connecting passengers, pooled service requests, customer time windows, and robotic tasks—often introduce intractable structures. At the same time, decision-makers must balance multiple, often conflicting objectives across stakeholders, including efficiency, reliability, and equity, while accounting for dynamic and stochastic demand patterns. The project addresses these challenges through a combination of advanced optimization methods (e.g., flow-based models that strengthen tractability, decomposition methods), learning-enhanced approximations (e.g., embedding-based representations of complex interactions, passenger flow estimation using generative techniques), and artificial intelligence (e.g., AI-powered digital twin simulations). Together, these approaches enable scalable, real-time decision-making in complex environments, while explicitly capturing trade-offs between operational performance and user-centric outcomes, such as passenger experience, system utilization, and service responsiveness.

The SMART-T7 team is led by Professors Alexandre Jacquillat, Jinhua Zhao, Hamsa Balakrishnan, Amedeo Odoni, and Jason Jackson from MIT, and Professor Hai Wang from Singapore Management University.

The successful candidate will work closely with Professors Alexandre Jacquillat (MIT), Hai Wang (Singapore Management University) and Bo Lin (National University of Singapore). The broad objective of the research will be to harness the power of operations research, AI, data science, and other disciplines to enhance the efficiency and effectiveness of human-machine systems. The primary application areas are smart city operations and robotic fleet management. The research will work at the interface of large-scale optimization, data-driven decision-making, the AI-optimization interface, and digital twin simulation. We invite applicants who hold or about to get a doctoral degree in Operations Research, Computer Science, Artificial Intelligence, Data Science, Industrial Engineering, or other related disciplines. We particularly welcome candidates with expertise and experience in the integration of AI and decision-making:

Key Responsibilities

• Collaborate with the project team and other researchers to design, implement, and evaluate research projects.
• Publish research results in top-tier journals and conferences and disseminate research findings through presentations and other means.
• Mentor graduate and undergraduate researchers involved in related projects.
• Assist with grant writing, project management, and administrative research duties.
• Perform other duties as needed. 

Requirements

• Ph.D. in Operations Research, Computer Science, Artificial Intelligence, Data Science, Industrial Engineering, or other related disciplines by the start of the appointment.
• Expertise 1: Experience with general methods in decision-making under uncertainty, including integer and combinatorial optimization, stochastic and robust optimization, dynamic programming, large-scale optimization, and simulation-based optimization; and/or
• Expertise 2: Experience with general methods in artificial intelligence and machine learning, including reinforcement learning, deep learning, generative AI, multimodal AI, and LLM.
• Experience in the integration of artificial intelligence and decision-making.
• Strong publication record in top-tier conferences and journals.
• Excellent communication and collaboration skills.

To apply, please visit our website at: https://portal.smart.mit.edu/careers/career-opportunities

Interested applicants are invited to send in their full CV/resume, cover letter and list of three references (to include reference names and contact information). We regret that only shortlisted candidates will be notified