Postdoc in causal inference at University

We are looking for a post-doctoral research fellow with a strong mathematical and/or computer science background to work with Prof Luc Berthouze and Dr George Parisis on the highly-interdisciplinary project “Rethinking large-scale network management through the lens of neuroscience”.

The project aims to transform the way ICT networks are being conceptualised for management, by developing a data-driven characterisation of emerging dependencies between ICT components inspired by recent neuroscientific paradigms used to study the brain and allowing to capture and act upon the functional impact of complex and changing interactions across layers and processes.

In the first phase of the project, the PDRA will develop and implement methods for inferring time-varying latent inter-dependencies based on events emitted, processed and stored in modern network and service deployments. Conceptual challenges to be met include the presence of multiple time scales as well as hierarchical organisation.

In the second phase of the project, the aim is to disambiguate hypothetical causal structures from the above statistical dependencies, with the view to provide interpretable and actionable insights. Use-case scenarios considered will be failure prediction and root-cause-analysis.

We are looking for a researcher with a proven record of developing and deploying machine learning / mathematical and statistical modelling for network inference in large-scale complex systems (e.g., biological, social or technological networks). Strong technical skills are required. Befitting the interdisciplinary and high-impact nature of the project, the candidate should be willing to engage with both academic and industrial partners.