Research Engineer (1-year contract), SCIS

• 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.

Other Information

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