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PhD student position in modelling and verification of software systems, London (England)

Applications are invited for a PhD studentship at University College 
London, under the supervision of Prof. Alexandra Silva and Dr. Matteo 
Sammartino.

The start date is flexible. It should be in September 2018 at the latest.

The studentship is funded by the UK Research Institute in Verified 
Trustworthy Software Systems, and will be carried out within the 
Programming Principles, Logic and Verification (PPLV) group 
(http://pplv.cs.ucl.ac.uk/). The PPLV group offers an exciting research 
environment, with outstanding connections with cutting-edge industry.

Potential applicants are encouraged to contact Prof. Silva 
(alexandra.silva@ucl.ac.uk) and Dr. Sammartino (m.sammartino@ucl.ac.uk) 
for further information and expressions of interest. Applications should 
be made via the UCL evision system:

https://evision.ucl.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RRDCOMSING01&code2=0025


=================== PROJECT DESCRIPTION ===================

Our society is increasingly reliant on complex networking systems, 
consisting of several components that operate in a distributed/concurrent 
fashion, exchange data that may be highly sensitive, and are implemented 
with a mix of open and closed-source code. Examples are Software Defined 
Networks, cloud computing systems, Internet of Things and others.

As the complexity of these systems increases, there is a pressing need of 
methods and tools to automatically verify security and privacy properties. 
High quality models ? able to express all the behaviours of interest ? are 
of paramount importance to this aim. However, it is often the case that 
the task of building a model is performed by humans and in a short span of 
time ? if it is performed at all ? and as such can be error-prone and 
inaccurate.

The goal of the PhD project is to develop techniques and tools to automate 
the modelling and verification of networking software systems. The novel 
idea is to rely on the model learning paradigm, originally proposed in 
artificial intelligence, to automatically build an automaton model of a 
running system in a black-box fashion -- purely via interactions with the 
running system.
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