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MOnitoring Outbreak events for Disease surveillance in a data science context (MOOD) explores the extraction, analysis, and visualization of health-related data (including also environmental data) to enhance the utility of event-based epidemiological surveillance. INESC-ID is hiring two researchers to work on work-package 3 within the project, specifically targeting tasks related to: Natural language processing for the extraction of information related to disease outbreaks from online media news. Specific tasks to be researched within this context include (a) key-phrase extraction, and (b) text geo-coding/geo-parsing. Deep learning methods for time series modelling and forecasting, envisioning the application of recurrent neural networks and/or state-space models to time series of disease counts.