Research Scientist (1-year contract), SCIS
Research Scientist (1-year contract), SCIS
Singapore Management UniversityFresher
- Posted 9 hours ago
- Be among the first 10 applicants
Job Description
Responsibilities
- This position is for School of Computing and Information Systems (SCIS).
- Lead and develop independent research directions within the project, including problem formulation, methodological development, and empirical validation.
- Publish research findings in top-tier journals and conferences in Operations Research, AI, and related fields.
- Disseminate research outcomes through presentations and other scholarly channels.
- Contribute to translating research into practice through engagement with policy and industry stakeholders. Mentor graduate and undergraduate researchers involved in related projects.
- Support grant management and project coordination activities.
- Other duties as assigned.
- Applicants should indicate one or more areas of interest aligned with the following themes:
- Pillar 1: Supply Chain, Logistics, and Mobility Systems
- Human-centered demand forecasting and adaptive inventory management.
- Fairness-aware and multi-objective optimization for resource allocation.
- Multi-agent and human-machine collaboration in logistics and transportation systems.
- Pillar 2: Emergency Response and Crisis Management
- New paradigms for emergency responders and rescue systems.
- Transfer learning and diffusion-model learning for diffusion/spread processes.
- Human-centered contact tracing and crowd management.
- Pillar 3: Healthcare Operations and Policy
- Data-driven modeling of healthcare demand, capacity, and congestion.
- Design of operational policies under uncertainty (e.g., screening, triage, resource allocation).
- Integration of behavioral, clinical, and system-level considerations in healthcare delivery.
- PhD (completed by start-date) in Operations Research, Industrial Engineering, Computer Science, Artificial Intelligence, Transportation, Logistics, or a closely related field.
- Strong background in optimization (e.g., integer/combinatorial optimization, stochastic/robust/online optimization, network models, queueing systems, simulation-based optimization).
- Strong background in machine learning (e.g., representation learning, sequence/time-series modeling, probabilistic modeling, or learning under distribution shift).
- Proven research track record (publications, strong writing, ability to frame contributions clearly).
- Solid programming skills; proficient in using AI tools for research.
- Ability to work independently, manage research milestones, and communicate effectively in a collaborative environment.
- Preference for candidate who has
- Experience with large-scale, constraint decision problems (e.g., operations, mobility, logistics, healthcare, or emergency response)
- Experience with multi-agent systems, multi-objective optimization, fairness-aware modeling, or human-centered decision-making approaches
- Experience with reinforcement learning, diffusion or spread models, explainable AI, transfer learning, or learning under non-stationarity or distribution shift
- Experience in developing reproducible research codebases (e.g., version control, experiment tracking, and documentation)
More Info
Key Skills
multi-agent systems
AI tools
explainable AI
transfer learning
fairness-aware modeling
reproducible research codebases
constraint decision problems
multi-objective optimization
About Company
Skills:
Version Control, multi-agent systems, experiment tracking, explainable AI, transfer learning, Documentation, human-centered decision-making approaches, fairness-aware modeling, reinforcement learning, AI tools for research, diffusion or spread models, large-scale constraint decision problems, multi-objective optimization
Skills:
Python, Ubuntu, C++, Linux command line, Robotics simulation, Robot navigation, VLA training, Annotation of training data, Machine learning for robotics, data acquisition, Integration of VLA models into robotics simulators, Simulation, Model training, Finetuning of models, Social force modeling