PhD in Computing Science: Actionable Information Finding from Crisis Big Data through Structured Collaboration between AIs and Volunteers

The Glasgow Information Retrieval group is looking for motivated students interested in our doctoral program. We are looking for a PhD student to work on emerging machine learning challenges in the emergency management domain to support response efforts during natural disasters (e.g. flooding, earthquakes or hurricanes). A successful student taking this opportunity will work with both public reports from first responders as well as high volume of social media data, working to improve the situational awareness of response personnel in the command and control centre during disasters.

The broad aim of this PhD programme is to examine and extend state-of-the-art active learning and artificial intelligence algorithms (e.g. new neural network architectures) when integrated with volunteering efforts (crowdsourcing), with the goal of identifying and cross-referencing actionable information from social media with on-going response activities. This will involve learning about how machine learning algorithms evolve over time and can be directed/tuned through human input, as well as learning about emergency response working practices and what makes information valuable during emergency situations.

Environment: The successful candidate will enrol as a PhD student at the School of Computing Science (Information, Data, Analysis Section), University of Glasgow, under the supervision of Dr Richard McCreadie. The successful candidate will be based in the Glasgow Information Retrieval Group, and will be expected to collaborate with experts in Big Data processing, Machine Learning from across the IDA Section. The successful candidate will have access to a state-of-the-art cluster of machines, including a cluster of new GPU servers, as well as terabytes of historical social media data.

Skills: The ideal candidate will have a strong background in Computer Science and some background in Statistics. In particular, the student is expected to have strong programming skills, some prior experience of machine learning and/or crowdsourcing, a good command of English and team working skills.

Eligibility: Full funding is provided for EU/UK students (standard home/EU fees and stipend rates included). Non-EU/UK students can apply, however they would be required to pay the difference between the home/EU and international fee.

Contact Information: For further information, interested candidates can contact Richard McCreadie ([Email Address Removed])

How to Apply: Please refer to the following website for details on how to apply:
http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/