Automation in Air Traffic Control (ATC) is gaining an increasing interest. Possible relevant applications are in automated decision support tools leveraging the performance of the Air Traffic Controller (ATCO) when performing tasks such as Conflict Detection and Resolution (CD & R). Another important area of application is in ATCOs training by aiding instructors to assess the trainees strategies. From this perspective, models that capture the cognitive processes and reveal ATCOs work strategies need to be built. In this work, we investigated a novel approach based on topic modelling to learn controllers work patterns from temporal event sequences obtained by merging eye movement data with data from simulation logs. A comparison of the work phases exhibited by the topic models and the Conflict Life Cycle (CLC) reference model, derived from post-simulation interviews with the ATCOs, indicated that there was a correspondence between the phases captured by the proposed method and the CLC framework. Another contribution of this work is a method to assess similarities between ATCOs work strategies. A first proof-of-concept application targeting the CD & R task is also presented.