Each PhD produced a final report (also published as Engage D5.18-D5.27). PhD abstracts and additional material, along with links to the published PhD theses are available here.

PhD final reports
(D5.18) P1. Decision support system for airline operation control hub centre (‘DiSpAtCH’)
(D5.19) P2. Trajectory planning for conflict-free trajectories: a multi agent reinforcement learning approach
(D5.20) P3. Detection, classification, identification and mitigation of GNSS signal degradations by means of machine learning
(D5.21) P4. Machine learning for aircraft trajectory prediction: a solution for pre-tactical air traffic flow and capacity management
(D5.22) P5. Deep Multi-Agent Reinforcement Learning Applications in ATM
(D5.23) P6. Integrating weather prediction models into ATM planning (‘IWA’)
(D5.24) P7. Advanced statistical signal processing for next generation trajectory prediction
(D5.25) P8. A pilot/dispatcher support tool based on the enhanced provision of thunderstorm forecasts considering its inherent uncertainty (‘STORMY’)
(D5.26) P9. Second generation agent-based modelling for improving APOC operations
(D5.27) P10. Resource constrained airline ground operations optimizing schedule recovery under uncertainty