Job Description
Airports are increasingly complex socio-technical systems in which efficient coordination of airside, landside, and even terminal operations is critical to maintaining safety, efficiency, and the passenger experience. As a major global aviation hub, Singapore must continuously enhance its airport management capabilities to support growing traffic demand, operational resilience, and sustainable airport operations. Achieving this requires advanced operational concepts, data-driven decision support, and integrated management of airport resources across stakeholders.
To strengthen national capabilities in airport systems research, we are advancing the TAM and CONOPS studies under collaborative initiatives with aviation stakeholders. These efforts aim to develop next-generation operational frameworks that integrate airport stakeholders, optimise resource allocation, and improve situational awareness and decision-making across the airport ecosystem.
We are seeking highly motivated Research Scientists to conduct cutting-edge research in airport operations, TAM, and CONOPS development. The role focuses on analysing complex airport operational processes, developing data-driven models, and designing future airport operational concepts to enhance efficiency, resilience, and collaboration among stakeholders, including air traffic management, airlines, ground handlers, and airport operators.
Successful candidates will work within a multidisciplinary team of researchers and engineers to develop advanced operational models, simulation tools, and optimisation frameworks that support airport-wide decision making. This includes studying airport operational workflows, identifying system bottlenecks, and designing integrated solutions for areas such as aircraft turnaround, ground movement, towing operations, gate and stand allocation, and disruption management.
Beyond research innovation, you will play a key role in translating research insights into operational decision-support tools and demonstrable prototypes, working closely with industry partners and operational stakeholders to ensure the relevance and deployability of developed solutions.
This work contributes directly to strengthening Singapore’s position as a future-ready global aviation hub, enabling more efficient, resilient, and intelligent airport operations through data-driven research and operational innovation.
Job Requirements
- PhD degree in Operations Research, Industrial Engineering, Systems Engineering, Computer Science, Computer Engineering, Data Science, Electrical Engineering, Mechanical Engineering, or a related discipline with rich AI experience.
- Strong programming and data analysis skills, preferably using C/C++, JAVA, or similar.
- Experience in data analysis, cleaning, and visualisation, particularly for real-world sensor data (e.g., surface radar, GPS, and weather data).
- Experience in simulation, optimisation, or decision-support systems applied to transportation or airport operations is highly desirable.
- Experience in process modelling, operational concept development, and workflow analysis for large-scale operational systems.
- Strong background in airport operations, air transport systems, or complex system modelling, with experience in operational analysis and systems-level thinking is a plus.
- Demonstrated ability to conduct independent, high-quality research and translate ideas into deployable prototypes.
- Strong communication and collaboration skills, with the ability to work effectively with domain experts and operational stakeholders.
- A strong sense of ownership, research integrity, and the ability to deliver high-quality work under tight timelines.
- A genuine passion for advancing AI technology and translating research outcomes into real-world operational impact.
The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.
