Studentship Vacancies

The Open University's Knowledge Media Institute (KMi) is a distinct research unit within the Faculty of STEM, and is home to internationally recognized 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 30 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.

We currently have the following vacancies for full time PhD studentships.


Additionally, we are offering a fully-funded studentship commencing February 2018. Applications are invited from UK, EU and ROW for a full-time, 3-year study on the following PhD topics. For further information, contact details are provided for each topic.

Faculty of Science, Technology, Engineering and Mathematics (STEM)
Stipend: £43,659 (£14,553 per year) plus fee bursary, Ref: 10745
Based in Milton Keynes

Please make a choice of one from these following topics:

  • Interactive live webcasting for distance learning
    Distance learning involves embedding the use of online communication tools as a means for supporting students’ engagement in group learning activities. A studentship in this area could study the application of synchronous technologies within OU modules, develop further applications and/or evaluate the impact of these technologies on the student experience.
    Contact: Trevor Collins.
  • Technology enhanced fieldwork learning
    Practical science fieldwork provides authentic learning opportunities that enable students to apply their learning and develop field skills, as well as effective teamwork skills that improve employability. A studentship in this area could focus on the design and development of activities or technologies and/or the evaluation of technology enhanced field courses.
    Contact: Trevor Collins.
  • Understanding and forecasting the spreading of research concepts
    The candidate will have to develop new methodologies for identifying research concepts (e.g., technologies, approaches, theories), analysing their spreading across research fields, and mining significant patterns that could be used to forecast their future development. The aim is to produce a system for supporting researchers by suggesting relevant new concepts and research ideas.
    Contact: Enrico Motta and Francesco Osborne.
  • Exploratory search in large heterogeneous data hubs
    Exploratory search solutions have so far primarily focused on supporting users in locating and making sense of information in large homogeneous repositories. With the emergence of large scale data portals, such as the MK Data Hub, the need has arisen for novel solutions effectively supporting users in exploring large heterogeneous repositories, comprising thousands of different (but potentially related) data sets. This research will require the design and development of novel exploratory solutions, which will comprise not only new user interface paradigms but also novel intelligent data aggregation and abstraction techniques to facilitate retrieval and sensemaking.
    Contact: Enrico Motta and Francesco Osborne.
  • Hate speech detection and prediction on social media
    This PhD project will involve developing computational methods for analysing how hate speech originates and spreads in the Web, what is its impact in different contexts (education, politics, journalism), and which intervention strategies can be considered to stop its initiation and propagation.
    Contact: Miriam Fernandez.
  • Analysing healthcare-focused social media communities for disease support
    This PhD project will investigate how heath social media communities and groups can provide support to patients, particularly patients with chronic diseases (diabetes, Parkinson, allergies) and health care professionals. The project will involve developing computational methods for analysing patients’ needs as well as the usefulness of community discussions and their potential application for enhancing health care procedures.
    Contact: Miriam Fernandez.
  • Visual Food Log Analysis
    To explore boundaries and limits of automated analysis of personal visual food logs. This project tries to carry out as much analysis as possible from visual food logs with little to no manual interference, mainly through computer vision and machine learning technologies, in particular deep learning.
    Contact: Stefan Rüger.
  • Realtime shape analysis
    This project researches and implements a state of the art shape search engine based on recently developed shape feature vectors. With this you can for example build a video search engine for “The Simpsons”; search for shapes in plant, butterfly or design databases; or carry out live analysis on a video of fingers playing the piano. Computer vision and machine learning will be key ingredients for this topic.
    Contact: Stefan Rüger.
  • Misinformation and fake news on social media
    Digital misinformation is a key challenge to modern societies, and is compromising our ability to form informed opinions about various critical issues relating to politics, health, environment, and economy. Although misinformation is a common problem in all media, it is exacerbated in digital social media due to the speed and ease in which they are spread, and the difficulty of providing countervailing corrective information. This PhD project will use computational approaches to investigate the spread of online misinformation (e.g., fake news, rumours, disinformation), to reach a better understanding of their flow dynamics, countering methods, and related socio-technical challenges and opportunities.
    Contact: Harith Alani.
  • Investigating Worldwide Inequality and Bias in Knowledge Communities
    Although recent research has investigated biases in relatively small-scale or localised communities for understanding how they discriminate particular members, the availability of large-scale datasets makes it now possible to better understand such biases on much larger, non-localised communities. The aim of this PhD is to investigate the multiples biases (e.g., socio-cultural, economical, gender and geographical biases) that shape knowledge oriented communities such as question answering websites, research communities (e.g., conference authors) and collaborative wikis. A key goal of the research is to measure and forecast the impact of these biases on the knowledge output generated by those communities, by developing and applying novel machine learning methods (e.g., deep learning).
    Contact: Harith Alani and Grégoire Burel.
  • Checking claims made in scientific literature
    As the numbers of scientific papers published grow ever larger, the quality of scientific output is increasingly rated based on bibliometrics. It is important to better understand the use of citation in scientific writing. This PhD project will use machine learning and natural language processing to analyse various aspects of citation usage in scientific writing, for example, checking the extent to which a cited work indeed makes the knowledge claims that they are been cited for, or comparing how different categories of papers are cited (e.g., historical vs current papers; review vs contribution papers; papers authored by the current authors [self-citations] or others...).
    Contact: Advaith Siddharthan.
  • Scrutable Machine Learning
    As computers make more and more decisions for us based on big data and machine learning approaches, there is current interest within several disciplines on how to explain the reasons for such decisions to users. This PhD will consider current learning frameworks that are typically used as black boxes (e.g. deep neural networks for classification or matrix factorization for recommender systems), and investigate how the output of these black boxes can be explained to users through language or visualisation.
    Contact: Advaith Siddharthan.
  • Discovering facts to support claims
    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.
    Contact: Petr Knoth and Zdenek Zdrahal.
  • Large-scale real-time aggregation and synchronization of web & knowledge resources
    This PhD will look into existing standards and protocols for web & knowledge resource synchronization, will understand their limitations at both the provider and consumer side, and will develop, test and profile new or enhance existing protocols for efficient real-time synchronization of these resources.
    Contact: Petr Knoth and Zdenek Zdrahal.
  • Identifying and predicting research & innovation trends
    This PhD will work with millions of research publications, patents, social network data and other datasets to develop new algorithms for identifying, analysing and predicting emerging research areas and innovations.
    Contact: Petr Knoth and Zdenek Zdrahal.
  • Reproducibility of text and data mining workflows
    Reproducibility, i.e. the ability of an entire experiment or study to be rerun obtaining the same results, is one of the key challenges scientists are facing today, with only a minority of research studies being reproducible. This PhD will analyse the limitations of existing methodologies, frameworks and workflow engines, and will aim to advance the state-of-the-art in the area of developing reproducible text and data mining workflows.
    Contact: Petr Knoth and Zdenek Zdrahal.
  • Crowdsourcing Data Mining: Collaboratively Generating Meaningful Actionable Insights from Big Data Sources
    Human judgement and sensemaking is key to understanding which data patterns and insights emerging from data mining are really meaningful and actionable. Research shows that collectives often outperform humans in many complex tasks, hence our question: Can crowds outperform domain experts in data science analysis? Building on research on Data Science and Collective Intelligence this PhD project aims at studying and developing a collaborative platform to support the human sensemaking process of mining big data.
    Contact: Anna De Liddo.
  • Collective intelligence and Explainable AI interfaces for the Common Good
    Machine intelligence has now open up society to issues of cognitive warfare, which we know very little about but are already shaping the future of our countries. The question is then: what type of “intelligent” systems do we need and we want to help building? How can we use advancements in machine intelligence and other forms of crowdsourcing, crowdfunding and collective intelligence to the purpose of improving the common good and develop civic intelligence rather than reinforcing the status quo? This PhD project will investigate these very questions and will propose new collective intelligence methods and tools which building on explainable AI approaches will facilitate consensus building and democratic decision-making in large-scale deliberation contexts.
    Contact: Anna De Liddo.
  • Linked Data and Distributed Ledgers
    The Semantic Web is a set of technologies designed to support the effective publication, integration and analysis of data on the Web based on a machine-readable representation of its meaning. Semantic Web standards such as RDF and OWL underpin the idea of a Web of Data built on the same model of open publication and linking which defines the human-readable Web of Documents, and enable the flexible interoperability and reuse of Web services, applications and data sources. Distributed ledgers are replicated, shared and synchronised digital data geographically dispersed over multiple sites possibly including multiple institutions. A blockchain is a specific type of distributed ledger where an ever-growing list of records, called blocks, are linked together to form a chain – hence the term ‘blockchain’. The first blockchain was conceived by Satoshi Nakamoto in his white paper as the basis for Bitcoin the most famous blockchain based crypto-currency. After Bitcoin, Ethereum is the best known blockchain platform, which aims to be an open platform to support the development and use of decentralised applications. Unlike Bitcoin the programming language available on the Ethereum platform is Turing complete so that general applications can be run on what the founders call a ‘world computer’. There is a wide scope for research into the interaction and possible connections between the Semantic Web and Distributed Ledgers or blockchains, from Linked Data indexing of ledgers, to RDF data storage and querying using distributed ledgers as backends to ensure data integrity and trustworthiness. We welcome proposals for PhD projects in this area.
    Contact: Allan Third and John Domingue.
  • Citizen interpretation of digital cultural artefacts and data
    This project is concerned with using semantic technologies to support and capture citizen interpretations of digital cultural heritage resources. There are many examples of using technology to enrich visitor experiences with artefacts in virtual and physical museum spaces. For example technology such as augmented displays, QR codes, beacons and RFID have been used to communicate or assist visitor interpretation of an object. However, technologies are rarely used to directly assist visitor interpretation and capture these interpretations for later use. The project will look at how technologies building on Linked Data and semantic web tools can be used to support active interpretation.
    Contact: Paul Mulholland.

We strongly recommend that you contact the researcher(s) associated with the topic to discuss your interest prior to writing your proposal.

You will have a first or upper second class degree from a UK university or the overseas equivalent and ideally a relevant Master’s 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. All applications are assessed as to their quality, the fit with The Open University research priorities and the availability of supervisors in the relevant field.

Closing date: 1 November 2017

Interviews: to be advised

For detailed information and how to apply call the Recruitment Co-ordinator on +44 (0)1908 654774 or email kmi-recruitment@open.ac.uk quoting the reference number.

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.

Applications should be sent by email to research-degrees-kmi@open.ac.uk (please CC kmi-recruitment@open.ac.uk 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.

About KMi

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Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
MK7 6AA
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support

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