The problem of situational understanding involves recognising existing situations ("what's currently happening?") and drawing inferences about possible future situations ("what might happen next?"). Deep neural networks (DNNs) for activity recognition have emerged as effective technologies in recent years for recognising existing situations. Approaches such as 3D convolutional neural networks (3D CNNs) and long short-term memory networks (LSTMs) can deal with the problem of recognising situations that are changing in real-time; however, they require large amounts of training data, making them problematic for recognising rare or unusual situations. For the second aspect -- drawing inferences about possible future situations -- DNNs also suffer serious limitations, due to their restricted ability to deal with cause-and-effect and open-world (unseen) situations. Knowledge graphs offer a way to deal with these limitations, to augment DNN capabilities with background or prior knowledge. Currently, however, effective integration of knowledge graphs with DNNs is limited and often shallow.
This PhD will look at deeper ways to integrate the benefits of DNNs and knowledge graphs, applied to the problem of situational understanding. The project will be conducted in collaboration with Cardiff University's Crime and Security Research Institute, which will provide access to real-world case studies such as managing the flow of misinformation in online networks, and rapid decision-making in "front line" contexts.
The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
The PhD is based at Cardiff University School of Computer Science and Informatics, and the research theme is: T3 - novel mathematical, physical and computer science approaches.
Eligibility
The scholarships are open to UK/home and international candidates. For more information on eligibility, please visit the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website
Entry Requirements
A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Candidates should be interested in AI and big data challenges, and in the research theme - novel mathematical, physical and computer science approaches.
Applicants whose first language is not English must demonstrate their proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component. (https://www.cardiff.ac.uk/study/international/english-language-requirements)
Applications
Please visit the CDT website http://cdt-aimlac.org/ and follow the instructions.
Applicants should apply for postgraduate study via the Cardiff University webpage: https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics,
Select the programme Doctor of Philosophy in Computer Science & Informatics with a start date of 1st October 2021, and upload these documents with your application:
• your CV
• a personal statement/covering letter
• two reference letters
• current academic transcripts
In the research proposal section of your application, please specify the project title and supervisors of this project. If you are applying for more than one project, please list the individual titles of the projects in the text box provided. In the funding section, please select ’I will be applying for a scholarship/grant’.
To complete your application please email a pdf(s) of your application to [Email Address Removed]
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