Research Engineer (1-year contract), SCIS

Singapore Management University; Singapore; On-site; SGD 48,000 - 66,000; Full time

Job summary

Singapore Management University in Singapore is seeking a driven researcher to design and develop world models that learn compact representations of VRP states and trajectories. You will integrate these models with neural solvers, planning, search or reinforcement learning to support decision making.

The role involves implementing robust neural and traditional optimization baselines and evaluating them on synthetic and real-world VRP datasets, and preparing research papers, technical

Qualifications

• Master degree in Computer Science, Information Systems or related field with AI focus.
• At least one year of research or development in RL, combinatorial optimization or related area.
• Knowledge of world models, generative modelling, vehicle routing problems, neural solvers or graph nets.
• Ability to design rigorous experiments, build benchmarks and reproduce findings.
• Strong foundations in algorithms, data structures and math reasoning.
• Publications or project experience in AI/optimization is advantageous.

Responsibilities

• Design and develop world models that learn compact representations of VRP states and trajectories.
• Integrate models with neural solvers, planning, search or reinforcement learning for decision making.
• Implement strong neural and traditional optimization baselines and evaluate on datasets.
• Prepare research papers, technical documentation and reusable code.
• Undertake other duties as assigned.

Skills

Mastery in AI/ML

Reinforcement learning

Combinatorial optimization

World models

Graph neural networks

Experimental design

Education

Master degree in CS/IS

Tools

Python

PyTorch

TensorFlow

Job description

• This position is for School of Computing and Information Systems (SCIS).
• Design and develop world models that learn compact representations of VRP states, transitions and solution trajectories.
• Integrate learned world models with neural solvers, planning, search or reinforcement learning methods for decision-making.
• Implement strong neural and traditional optimization baselines and evaluate them on synthetic and real-world VRP datasets.
• Prepare research papers, technical documentation and reusable code.
• Undertake other duties as assigned.

Responsibilities

• This position is for School of Computing and Information Systems (SCIS).
• Design and develop world models that learn compact representations of VRP states, transitions and solution trajectories.
• Integrate learned world models with neural solvers, planning, search or reinforcement learning methods for decision-making.
• Implement strong neural and traditional optimization baselines and evaluate them on synthetic and real-world VRP datasets.
• Prepare research papers, technical documentation and reusable code.
• Undertake other duties as assigned.

Qualifications

• Minimum master degree in Computer Science, Information Technology, Information Systems or closely related disciplines with particular focus on Artificial Intelligence.
• At least one year of relevant research or development experience in reinforcement learning, combinatorial optimization or a related area.
• Working knowledge of world models, generative modelling, combinatorial optimization, vehicle routing problems,neural solvers or graph neural networks.
• Ability to design rigorous experiments, build benchmarks, analyze results and reproduce research findings.
• Solid foundations in algorithms, data structures and mathematical reasoning.
• Research publications or substantial project experience in AI or optimization are advantageous.
• Clear written and verbal communication skills, including the ability to prepare technical reports and papers. Self-motivated and able to work independently while collaborating effectively in a small academic research team.