Analysis of events data (PhD/Postdoc)
Event modelling concerns the analysis of ordered, timestamped data, with discrete states (or patterns) occurring at a particular point or points in the sequence. Examples include detecting faults or cyber-attacks in industrial processes and computer networks, predicting risk of fractures or disease flares in patients, learning of socio-political events through analysis of text data and even modelling the rate of taxi demand and hospital admissions.
We invite enthusiastic individuals to join our group at TU Kaiserslautern as a PhD student or postdoctoral researcher to work on a topic in this research area. Candidates should be interested in undertaking a project in the sphere of ’events data’, which includes one or a combination of the following sub-domains:
- event detection
- pattern recognition, anomaly and changepoint detection and process control: determining the occurrence events through recognizing patterns in historical data
- event prediction
- forecasting events, joint modelling of time-to-event and longitudinal data
- causal event modelling
- using structural models to answer counterfactual questions about the occurrence/non-occurrence of events
More information to follow.
If you would like to know more about this opportunity—both at PhD and at postdoctoral level—please get in touch.