Research Fellow (Air Transportation / Transportation Engineering / Statistics / Data Science / Operations Research / Applied Mathematics / Economics)

ATMRI at NTU is seeking a Research Fellow to conduct applied research in airport and airspace capacity, demand management, delay, operational performance, and sustainable aviation. The role will develop statistical, mathematical, causal-inference, simulation, and machine-learning methods using large-scale flight trajectory and operational datasets. Research may cover airport surface congestion, runway-system capacity, terminal airspace performance, delay propagation, multi-airport systems, performance benchmarking, and the impacts of capacity interventions. The successful candidate will develop reproducible models and decision-support methods, contribute to project milestones and high-impact publications, and work closely with academic, industry, airport, and regulatory stakeholders. Responsibilities: Conduct research on airport and airspace capacity, demand management, delay, congestion, and operational performance. Develop statistical, mathematical, econometric, causal-inference, simulation, and machine-learning models using large-scale aviation datasets. Analyse airport surface, runway, terminal airspace, and multi-airport system operations, including traffic-flow and delay-propagation mechanisms. Develop capacity-estimation, demand-forecasting, performance-benchmarking, and what-if assessment methods to support planning and operational decision-making. Investigate the operational, environmental, safety, and economic impacts of airport capacity interventions and sustainable aviation measures. Process, integrate, visualise, and validate flight trajectory, operational, weather, and other relevant transportation datasets. Translate research outcomes into practical tools, technical reports, stakeholder presentations, and recommendations for industry and regulatory partners. Support delivery of project milestones and contribute to high-quality journal and conference publications and research proposals. Collaborate with multidisciplinary academic, industry, airport, airline, and air navigation service provider stakeholders, and mentor students or junior researchers where appropriate. Requirements: Ph.D. in Air Transportation, Transportation Engineering, Statistics, Data Science, Operations Research, Applied Mathematics, Economics, or a related field. Expertise in statistical or mathematical modelling, causal inference, econometrics, simulation, machine learning, or spatiotemporal analysis. Strong programming skills in Python and/or R; experience with STATA, MATLAB, or SQL is advantageous. Experience with aviation, airport, airspace, flight trajectory, or transportation datasets. Strong publication record and good technical writing and communication skills. Ability to work in multidisciplinary teams and engage external and international stakeholders. We regret to inform that only shortlisted candidates will be notified.