Research Fellow (Optimization/Artificial Intelligence/Transportation/Aerospace) - Choa Chu Kang

The selected candidate will work on developing innovative optimization and Machine Learning models to address key challenges in the future airspace system, including traffic prediction, optimization, and safety enhancements.

Key Responsibilities:

• Conduct applied research in machine learning, deep learning, and reinforcement learning for optimizing airport operations and air traffic flow.
• Design and implement predictive models for runway capacity, taxiway configuration transitions, and gate related ATM performance indicators using real-world data.
• Conduct research on optimization techniques and machine learning models in Air Traffic Management systems/airport operations.
• Collect, process, and analyse air traffic data relevant to the project.
• Develop or use existing simulation platforms to validate the developed algorithms and models.
• Analyse simulation data, and create visualizations to support research findings.
• Design and build prototypes or intelligent tools for traffic flow management.
• Collaborate effectively with industry and academic partners to integrate AI models into ATM decision support systems.
• Participate in project meetings, prepare research reports, and contribute to high-impact publications in leading academic conferences and journals.
• Assist in drafting project proposals and preparing technical documentation.
• Support the Principal Investigator (PI) or team lead with project management tasks.

Job Requirements:

• PhD degree in Optimization, Artificial Intelligence, Transportation or Aerospace.
• Evidence of developing Machine Learning and Optimization models for ATM system.
• Strong programming skills in Python, Java etc.
• Good English writing and communication skills for research work.
• Able to work independently while contributing effectively in collaborative, multidisciplinary research teams.

We regret to inform that only shortlisted candidates will be notified.