Airline schedules are constantly being updated to deal with disruptions originating from crew absence, upstream delays, mechanical failures and other sources. A robust schedule is capable of dealing with- or absorbing negative effects of such unexpected events. Schedule robustness, accounted for proactively in the scheduling g phase, is extensively researched for the crew scheduling problem, but the subproblem of simulator-based crew training is neglected. Airlines periodically train cockpit crew members for legal licensing purposes (recurrent training) and crew conversion between fleets and ranks (conversion training). This operation involves up to thousands of working days for large airlines. Any disrupted training activity potentially impacts crew availability by means of missed due dates in case of recurrents or postponed employment in case of conversion. The vast cost and risk related to this problem has led to the development of a robust cockpit crew training schedule. First, an integrated Training Scheduling & Assignment Model (TS&AM) is developed to construct a training schedule. A Monte Carlo Simulation is then applied to generate disruption scenario’s, which are then solved using a rule-based recovery heuristic. The output of the recovery model is captured in features that translate into expected recovery cost, which are then used in a feedback loop. Two feedback variants are tested: (1) Proportional Feedback (PF) and (2) Neural Network (NN) feedback. The feature dependent expected recovery cost is then used in another cycle of the TS&AM to output a robust training schedule. As a final step, the robust training schedule is tested using the same simulation evaluation framework. Experiments have shown that a 16.75 percent improvement on recovery cost or cost of crew unavailability due to missed due dates can be achieved by learning ways of introducing schedule robustness. The lowered recovery cost also translates to a 1.11 percent overall cheaper operation of the training schedule.