The DiTEC (Digital Twin for Evolutionary Changes in water networks) project proposes an evolutionary approach to real-time monitoring of sensor-rich critical infrastructures that detects inconsistency between measured sensor data and the expected situation, and performs real-time model update without needing additional calibration. Deep learning will be applied to create a data-driven simulation of the system. The system is applied to water networks, where, in case of leaks, valve degradation or sensor faults, the model will be adapted to the degraded network until the maintenance takes place, which can take a long time. The project will analyse the effect on data readings of different malfunctions, and construct a mitigating mechanism that allows to continue using the data, albeit in a limited capacity. As part of the DiTEC project, the role of the PhD student will be to analyse historical and real-time sensor data, which includes parameters such as water speed, pressure, quality, network topology, and construct a number of deep learning (such as CNN and LSTM) models to explain and predict the behavior of the network short and long term.