Studentship Vacancies

Full time funded PhD Studentship (2 positions available)
KMi, Faculty of Science, Technology, Engineering and Mathematics (STEM)
Stipend: £14,777 pa plus fee bursary, Ref: 11074
Based in Milton Keynes

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, located at the OU 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.

Applications are invited from UK, EU and ROW for a full-time, 3-year funded studentship commencing October 2018, study on the following PhD topics. For further information, contact details are provided for each topic. We strongly recommend that you contact the researcher(s) associated with the topic to discuss your interest prior to writing your proposal.

Your proposal is to be based on the following topic:

Jump to a topic of interest:


Sketch search

This PhD project researches state-of-the-art shape search engines based on recently developed shape feature vectors and develops new ways of indexing and retrieving shape from video. With this you could, for example, build a "Simpson video search engine"; search by example images in plant, butterfly or design databases; or build automated identification tools for ecological platforms such as iSpot.

Tags: search engines, image retrieval, computer vision, machine learning.

Key skills:

  • Scientific curiosity, perseverance, strong programming skills (e.g., python, Java, C++);
  • Experience with machine learning and the corresponding mathematics;
  • Excellent verbal and written communication skills;
  • Ability to organise own work with minimal day-to-day supervision.

Contact: Stefan Rueger


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.

Tags: natural language processing, information extraction, machine learning, big data, social media.

Key skills:

  • Good knowledge of NLP, text-mining, information extraction or information retrieval;
  • Understanding of machine learning fundamentals;
  • Excellent programming and especially prototyping skills;
  • Ability to process large datasets;
  • Understanding of the research lifecycle (ability to state hypotheses, design new methods, implement prototypes, evaluate methods, interpret results, adapt methods, etc.);
  • Prior experience of data analysis and machine learning.

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.

Tags: semantic web, web protocols, software architecture, interoperability.

Key skills:

  • Good understanding of the interoperability concept, Web communication protocols and W3C standards;
  • Interest in clean software design and architecture;
  • Ability to design protocols and software architecture;
  • Excellent programming and especially prototyping skills;
  • Understanding of the research lifecycle (ability to state hypotheses, design new methods, implement prototypes, evaluate methods and interpret results, adapt methods;
  • Ability to process large datasets, test, profile, analyse and optimise software solutions;
  • A sense for precision and detail.

Contact: Zdenek Zdrahal and Petr Knoth


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.

Tags: text and data analytics, machine learning, big data, trend analysis.

Key skills:

  • Good knowledge of NLP, text-mining, information extraction or information retrieval;
  • Understanding of machine learning fundamentals;
  • Excellent programming and especially prototyping skills;
  • Ability to process large datasets;
  • Understanding of the research lifecycle (ability to state hypotheses, design new methods, implement prototypes, evaluate methods, interpret results, adapt methods, etc.);
  • Prior experience of data analysis and machine learning.

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.

Tags: open science, text and data analytics, natural language processing, reproducibility.

Key skills:

  • Good knowledge of NLP, text-mining, information extraction or information retrieval;
  • Understanding of machine learning fundamentals;
  • Excellent programming and especially prototyping skills;
  • Ability to process large datasets;
  • Understanding of the research lifecycle (ability to state hypotheses, design new methods, implement prototypes, evaluate methods, interpret results, adapt methods, etc.);
  • Prior experience of data analysis and machine learning.

Contact: Zdenek Zdrahal and Petr Knoth


Analysing the dark side of social media

Radicalisation, grooming, and fake-news on social media platforms are damaging our societies at multiple levels. You will research one related topic and develop methods to detect, predict, or counteract such online content and behaviour. You will join a multi-disciplined international consortium of researchers and practitioners, and travel to project meetings and related events across Europe.

Tags: social-media, computational-social-science, radicalisation, misinformation, fake-news.

Key skills:

  • Social media analysis;
  • Machine-learning;
  • Semantic-web;
  • Data science.

Contact: Harith Alani


Crowdsourcing Data Mining in Urban Informatics: Collaboratively Generating Insights from Big Data Sources in Human Smart Cities

As cities are been currently recognized as the future of humanity’s growth, understanding cities is becoming increasingly important to monitor and drive the health of cities development, toward sustainable environmental, economic and social models. New collective approaches often outperform domain experts in data science analysis and identifying emerging behaviours. Hence, our question: Can crowds be key factors and actors in understanding cities?

Building on research on Data Science, Urban Informatics and Collective Intelligence this PhD project aims at studying and developing a collaborative platform to support the human sensemaking process of mining big data in a smart city context. A specific attention will be put in developing new interfaces for citizen engagement with real-time, in-context, spatial interactions with the city, which feed into a visual analytics platform of collective “CitySense”-Making (defined as the improved collective capability to make sense of the City space).

Tags: Collective Intelligence, Crowdsourcing, Urban Informatics, Data Science, UI/UX Design, CSCW.

Contact: Anna De Liddo


Human-Machine Interfaces for Explainable AI

In matters of national security, international policy, and patient treatments, predictions cannot be accepted without validation. Still, often AI technologies use black box processes that are difficult even for experts to understand and explain. If we are to trust AI, we need to understand how machine judgement is formed and, most importantly, we need to explain such rationale in a way such that non-AI savvy subject matter experts can intuitively interrogate and scrutinise the rationale behind it.

This PhD project will produce cutting-edge human computer interfaces for Explainable AI (XAI). These are visual interfaces for non-expert users to make sense of and communicate to others the rationale of machine predictions. We will explore application problems such as machine assisted urban informatics, healthcare and finance. This research will enable the production of scientific evidence and new Explainable AI technologies to improve human trust and societal impact of AI technologies in real life decisional contexts.

Tags: AI & Machine Learning, Human-Machine Dialogue, UI/UX Design, CSCW.

Contact: Anna De Liddo


Communicating Complex Data

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.

Tags: Artificial Intelligence, Data Science, Natural Language Generation.

Key skills:

  • Computational Linguistics;
  • Artificial Intelligence.

Contact: Advaith Siddharthan


Co-learning between humans and machines

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.

Tags: Artificial Intelligence, Data Science, Technology Enhanced Learning.

Key skills:

  • Technology Enhanced Learning;
  • Machine Learning.

Contact: Advaith Siddharthan


Linked Data and Distributed Ledgers

We welcome proposals regarding the intersection between the Semantic Web and Distributed Ledgers, e.g., RDF indexing of blockchains, or Linked Data storage and querying using blockchains to ensure data integrity. Topics can be in any application domain, e.g., scholarly publishing, education, eHealth, etc.

Tags: Semantic Web, Blockchain, Linked Data, Distributed Ledger.

Key skills:

  • Software development;
  • Semantic Web skills/interest;
  • Blockchain skills/interest.

Contact: John Domingue and Allan Third


Blockchain for Education

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.

Tags: Blockchain, Education, Accreditation, Open Badges, ePortfolios.

Key skills:

  • Software development;
  • Education skills/interest;
  • Blockchain skills/interest.

Contact: John Domingue and Alexander Mikroyannidis


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.

Tags: data science, web science, social media analytics, hate speech.

Key skills:

  • Programming (Java, Python, R);
  • Machine Learning;
  • Statistics.

Desirable:

  • Big data technologies.

Contact: Miriam Fernandez


Analysing healthcare-focused social media communities for disease support

This PhD will investigate how heath social media communities and groups can provide support to patients with chronic diseases (diabetes, Parkinson, allergies) and health care professionals. The project will involve developing computational methods for analysing patients’ online discussions and their potential application for enhancing health care procedures.

Tags: data science, web science, social media analytics, healthcare.

Key skills:

  • Programming (Java, Python, R);
  • Machine Learning;
  • Statistics.

Desirable:

  • Big data technologies;
  • Healthcare background.

Contact: Miriam Fernandez


Modelling and predicting the spreading of ideas and technologies in research

The candidate will develop new methodologies for identifying research concepts (e.g., technologies, methods, theories) in the scientific literature, analyse the way they propagate across research communities, and use the results from this analysis to predict the trajectories of new research concepts across different communities. The ultimate aim of this work is to produce a tool able to alert researchers to relevant new research concepts emerging in other communities.

Tags: Research Communities, Data Science, Machine Learning, Scholarly Data, Semantic Web, Knowledge Extraction, Ontologies.

Key skills:

  • Proactivity and problem solving capabilities;
  • Ability to work to challenging targets and deadlines;
  • Good written and oral communication skills.

Desirable:

  • Experience with machine learning and data mining algorithms.

Contact: Francesco Osborne and Enrico Motta


Designing intelligent systems to integrate robots in smart urban environments

Autonomous mobile systems such robots are rapidly spreading outside the industrial and research domain, and will soon become an active part of (smart) city ecosystems. Despite the advances in both robotics and information technology, the integration of robots in urban environments is still at an early stage, mostly due to the inability of autonomous mobile systems to deal with heterogeneous knowledge sources in complex and large scale scenarios. In order to achieve this, methods combining Robotics, Artificial Intelligence, Knowledge Representation and Data Science need to be studied. We seek applicants interested in topics such as:

  • Management of heterogeneous sources of knowledge for robots;
  • Human-Robot Interaction in communities;
  • Robots and the Web/Internet of Things;
  • Ethical and societal implications of robotic technologies in smart urban environments.

Tags: Human-Robot Interaction, Smart Cities, Internet of Things, Urban Computing, Intelligent Systems, Data Science.

Key skills:

  • Proactivity and problem solving capabilities;
  • Ability to work to challenging targets and deadlines;
  • Good written and oral communication skills.

Desirable:

  • Experience with Intelligent Systems.

Contact: Ilaria Tiddi and Enrico Motta


Privacy, Discovery and Reuse in the Web of Data

The Web of Data defines a paradigm shift, as datasets are now published independently of a specific task need. As a result, we face a novel challenge, how to derive and express formally what it is that we can do with this data? A related problem concerns privacy. How can we characterise the features of datasets in order to exploit their potential without disclosing personal information?

We are interested in tackling these challenges and we seek applicants interested in developing new solutions that can facilitate an automatic characterization of the "affordances" of a dataset and/or enable privacy-aware data reuse

Tags: Web of Data, Data Science, Privacy, Data Management, Data Curation.

Key skills:

  • Proactivity and problem solving capabilities;
  • Ability to work to challenging targets and deadlines;
  • Good written and oral communication skills.

Desirable:

  • Experience with Data Science solutions.

Contact: Enrico Daga and Enrico Motta


Digital storytelling tools and techniques for engaging with cultural heritage

Cultural heritage institutions invest significant resource into the curation and production of exhibitions. This studentship will explore how that process can be supported and captured through digital technologies that enable visitors to access and engage with the curation process. The social layer that technology provides connecting visitors with objects, artists, events and curators also opens up opportunities to research the impact of social media within digital storytelling.

Tags: Museum informatics, digital storytelling, visitor participation.

Key skills:

  • Web development;
  • Semantic Web;
  • Evaluation.

Contact: Trevor Collins and Paul Mulholland


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: 17:00 on 12 April 2018

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.

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CONTACT US

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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