PhD. position. Data scientist / Machine Learning specialist

Water stress is currently a major concern in many regions of the World and is expected to worsen in the near future. Efforts to improve water management and minimize inefficiencies in distribution networks are essential to ensure adequate and sustainable water supply. The digitization of water meters and automation of data management have provided an unprecedented opportunity to gather massive data on the water cycle. Machine Learning (ML) and Artificial Intelligence (AI) techniques can use this valuable information to characterize, analyze and optimize the management of this key resource. The goal of this project is to rationalize the usage of water in a mid-size city using ML/AI approaches capable of detecting leaks and fraud, optimizing different processes along the water cycle, predict the distribution network response against different types of stressors and provide tailored services to end users.