The Open University's Knowledge Media Institute (KMi) is a distinct research unit within the Faculty of STEM, and is home to internationally recognised researchers in semantic technologies, data science, educational multimedia, collaboration technologies, artificial intelligence, cognitive science, and human-computer interaction. KMi offers students an intellectually challenging environment with exceptional research and computer facilities. KMi is located at the Open University headquarters in Milton Keynes, which is 45 minutes by train from central London. Past KMi PhD students pursue successful careers in academia and industry and have won a number of distinguished awards, including the 2016 and 2017 SWSA Distinguished Dissertation Awards.
You can get a feel for what it would be like to work in KMi from our careers page.
We currently have up to two vacancies for full-time PhD studentships starting on 1st October 2019 in the following topic areas; applicants should contact the named contact person for the topic of interest to get more information and guidance on developing their project proposal.
Please note that the deadline for submitting and application is March 15th 2019
Blockchains and Decentralised Systems
Blockchains and decentralised data analytics for verified personal data (ref: 11996)
The over centralisation of data on the Web has led Tim Berners-Lee to call the artifact ‘anti-human’. This PhD will investigate how a combination of blockchain and decentralised data technologies (e.g. Solid) can be used to give users control of their own data whilst ensuring that any data claims made are verifiable. For more info see http://blockchain.open.ac.uk
Keywords: blockchain, decentralised ledgers, linked data, knowledge graph, solid
Skillset: software and web development, blockchain skills/interest (especially Ethereum), linked data skills/interest
Contact: Prof John Domingue and Dr Allan Third
Blockchain for education (ref: 11996)
This PhD project will investigate the potential of blockchain technology on different aspects of education, for example accreditation, open badges, reputation building, ePortfolios and more. Blockchain is the technology behind crypto-currencies like Bitcoin, offering a publicly shared immutable ledger that can be used in many interesting and potentially revolutionary scenarios in education. For more info see http://blockchain.open.ac.uk
Keywords: blockchain, decentralised ledgers, education, accreditation, open badges, ePortfolios.
Skillset: software development, education skills/interest, blockchain skills/interest.
Contact: Prof John Domingue and Dr Alexander Mikroyannidis
Computational Social Science
Impact of online misinformation (ref: 11996)
Misinformation is compromising our ability to form informed opinions about various critical issues relating to politics, health, environment, economy, etc. This PhD will investigate a range of computational methods for measuring the impact of consuming misinformation by individuals and networks, and the effectiveness of various corrective measures.
Keywords: misinformation, social media
Skillset: programming, machine learning, large scale data analysis
Contact: Prof Harith Alani
Spread of misinformation (ref: 11996)
Social media platforms are regularly blamed for facilitating the spread of misinformation. However, the manipulation of information by newspapers and public figures, and their role in the spread of misinformation, are not receiving much attention. This PhD will device methods to track the spread of misinformation across multiple medias and networks, to better understand their global dynamics and sources of influence.
Keywords: misinformation, social media.
Skillset: programming, machine learning, natural language processing, network analysis
Contact: Prof Harith Alani
Analysing, identifying and preventing online hate (ref: 11996)
This PhD project will focus on studying online hate (misogyny, xenophobia, homophobia, extremism, radicalisation, mocking disabilities, etc.). In particular, we aim to study how it originates, how it spreads, what is its impact in different contexts (education, politics, journalism), and which intervention strategies are more effective in its prevention.
Keywords: online hate, data science, web science, social media analytics
Skillset: programming (java, python, R), machine learning, statistics, desirable - big data technologies
Contact: Dr Miriam Fernandez and Dr Tracie Farrell
Data ironing on Twitter (ref: 11996)
Communication on Twitter usually happens in little bursts of activity followed by longer calm periods. In this project you will develop novel methods for network analysis that can ‘iron out’ bursts of activity to focus on the important information being shared, and that can be applied on any temporal network. You will validate your methods on real datasets collected from Twitter.
Keywords: temporal networks, community detection, network centrality, network modelling, graph algorithms
Skillset: python programming, network algorithms, graph theory
Contact: Dr Danica Greetham
Data Science and Machine Learning
Discovering semantic relationships in large textual collections with applications to recommender systems for distance education and research (ref: 11996)
This PhD project will investigate personalised and non-personalised approaches to the discovery of related information, including research papers, topics, people and facts. It is expected that new methods will be developed that benefit from various forms of content relationship analysis, such as finding similar or contradictory information and discovering evidence. The methods may also be combined with new user-based approaches, such as collaborative filtering.
Keywords: recommender systems, machine learning, information retrieval, distance education
Contact: Dr Martin Hlosta
Discovering facts to support claims (ref: 11996)
With the rise of fake and misleading news, it is becoming harder for people to understand which facts can and which cannot be trusted. The goal of this PhD is to develop new methods to automatically discover evidence to support or refute claims.
Keywords: natural language processing, deep learning, information extraction, machine learning, big data, social media
Contact: Dr Petr Knoth
Large-scale information extraction from unstructured textual resources (ref: 11996)
The student is expected to work with millions of research papers extracting useful information, such as names of scientific methods, statistical tests performed, tables, graphs plus captions, basic metadata (title, affiliation, abstract, author names), conclusion sentences, innovation sentences, methodology sentences, algorithm descriptions, etc., from their text to assist in knowledge discovery and information retrieval. It is expected the student will focus on developing new information extraction methods making use of supervised and semi-supervised machine learning.
Keywords: natural language processing, information extraction, machine learning, deep learning, big data
Contact: Dr Petr Knoth
Supporting scientific research with knowledge graphs (ref: 11996)
Intelligent systems are needed to support researchers in managing and making sense of the vast amounts of scholarly information that are now available. The candidate will develop new methodologies for generating knowledge graphs of research concepts and exploiting them in order to produce smart analytics, recommend relevant scholarly resources, and generate new scholarly knowledge.
Keywords: software development, machine learning, data mining algorithms, semantic data representation, information extraction methods, proactive problem solving, challenging targets and deadlines, written and oral communication skills
Contact: Dr Francesco Osborne
The role of machine learning in computer vision for biodiversity monitoring (ref: 11996)
The role of machine learning in computer vision for biodiversity monitoring This PhD project explores machine learning algorithms, particularly convolutional neural networks, for computer vision tasks such as shape recognition or articulated movement extraction. The project will explore these methods on datasets that are relevant to automated biodiversity monitoring, such as photographs or short videos of animals, insects or plants in their natural habitat. The data might also come from monitoring stations or personal drones.
Keywords: convolutional neural networks, transfer learning, computer vision, biodiversity monitoring
Skills: Solid mathematical ability and excellent programming skills; basic knowledge of machine learning; basic knowledge of computer vision; general interest in research, curiosity and persistence.
Contact: Prof Stefan Rueger
Communicating complex data (ref: 11996)
There is widespread interest in communicating complex data to users. For example, as big data allows computers to make decisions for us these need to be explain to users, or, complex environmental data needs to be communicated to the general public in a manner that illustrates scientific insights and perhaps influences public attitudes. This PhD will consider the problem of communicating such examples of complex data through language or visualisation.
Keywords: artificial intelligence, data science, natural language generation
Skillset: computational linguistics, artificial intelligence
Contact: Dr Advaith Siddharthan
Co-learning between humans and machines (ref: 11996)
This proposal will explore opportunities and methodologies for co-learning between machines and humans in the context of citizen science projects, where members of the public contribute to scientific projects by providing and annotating scientific data. The focus will be on methodologies that allow machine and humans to help each other learn from data in such informal learning contexts.
Keywords: artificial intelligence, data science, technology enhanced learning
Skillset: technology enhanced learning, machine learning
Contact: Dr Advaith Siddharthan
New Media in Society
Personal and social storytelling for engaging with cultural heritage (ref: 11996)
The aim of this project is to design, develop and evaluate knowledge technologies to support individuals and groups in developing personal and social narratives in relation to cultural heritage resources. This studentship will explore how visitor’s experience of cultural heritage can be enhanced through technology in a personal and social digital space. The research will involve collaboration with museums and cultural institutions, and exploit existing digital collections as well as open data repositories. The resulting technologies may be trialled in formal or informal learning contexts.
Keywords: museum education, storytelling, knowledge technologies, mobile technologies, technology-enhanced learning
Skillset: human-computer interaction, design, web and mobile development, evaluation
Contacts: Dr Trevor Collins, Dr Enrico Daga and Dr Paul Mulholland
Improving social tolerance in online media discourse (ref: 11996)
The media commenting spaces that we see on the web today are flood. A combination of homophily and lack of content quality constantly degrade the balance and safety of online media discourse, up to undermining social tolerance. This PhD will develop disruptive new technologies to reduce polarization, support serendipitous knowledge discovery and improve social tolerance in online media commenting.
Keywords: online media commenting, digital journalism, social justice
Skillset: programming (web development - back and front end), basic statistics, strong drive toward developing social impact
Contact: Dr Anna De Liddo
Smart Living and Robotics
Energy efficiency for individual users (ref: 11996)
Based on smart meter (or similar monitoring equipment) data, we want to predict energy demand for individual users, small businesses or groups of users. This enables more efficient use of energy through smart control, storage, demand shifting and benefits/costs analysis of photovoltaics, electric vehicles, heat pumps, etc. In this PhD you will develop new algorithms for individual demand forecast based on fractal dimension, recurrence plots, different methods from machine learning and statistics and several data sets.
Keywords: smart meter data science, demand forecast, time series, machine learning, algorithms
Skillset: python programming, time series, basic statistics
Contact: Dr Danica Greetham
Towards a new generation of urban robots (ref: 11996)
This research topic concerns the development of a new generation of urban robots, situated within a wider information eco-system (e.g., a smart city infrastructure) and operating in complex dynamic environments. Our main objective is improving both object recognition and decision-making, by making use of a variety of knowledge sources and by integrating different types of reasoning techniques. We also seek novel solutions enabling robots to flexibly switch between different interaction modalities, in response to changes in the environment.
Keywords: robotics, semantic technologies, knowledge representation and reasoning, smart cities, interaction models, problem solving, object recognition
Skillset: Knowledge of robotics environments, computer vision algorithms, semantic technologies and machine learning methods, excellent programming and communication skills
Contact: Prof Enrico Motta
Equal Opportunity is University Policy
How to apply
It is strongly recommended that applicants contact the named contact point for the project of interest to get more information about the project in question. It is also important to read the online prospectus before downloading and completing the 10-page MPhil/PhD application form.
- Application form if you are resident in the UK or European Economic Area.
- Application form if you are resident elsewhere.
Applications should be sent by email to email@example.com (and also please CC the appropriate project leader(s) relating to your chosen topic proposal when submitting applications) and should include a covering letter, MPhil/PhD application form, a research proposal (a maximum of 2,000 words), and a full CV, giving contact details for two academic referees.
All applicants must have a first or upper second class degree from a UK university or the overseas equivalent and ideally a relevant Masters degree. Unless from a majority English-speaking country, non-EEA applicants will require an IELTS score of 6.5 with a minimum of 6 in each element of Listening, Reading, Speaking and Writing. IELTS Certificates are valid for a period of 2-years.