PhD studentships for September 2022

Engineering & Technology - UK Jobs

The School of Computing at Robert Gordon University is offering 5 exciting industry-lead PhD studentships for September 2022. Successful candidates will be joining the top modern university in Scotland (GUG 2022) and a school (School of Computing) that was listed as the top modern in Scotland for Computer Science (GUG 2022).

The computing school is internationally recognised for research in AI & Reasoning, Computational Intelligence, Immersive Tech and Machine Vision. We have a thriving research environment supported by national and international funding to bring together early-career and early-stage researchers across all areas to carry out leading-edge applied computer science research with high industrial and societal impact. You will be joining a lively, friendly and supportive research and teaching community within the school.

Duration and Funding

The duration of projects will be up to 36 months, commencing in October 2022. The studentship covers both tuition fees – at UK or International/Overseas level and a tax-free stipend (living allowance) of £15,600 per annum. The successful candidate will be required to pay for their Student Visa and travelling costs to Aberdeen; the stipend will start on commencement of studies.

The successful candidate will be required to relocate to Aberdeen as soon as possible to study, although studies may start remotely initially; this is an essential requirement of the studentship and is not open to negotiation.

Please do not apply if you are unable to relocate to Aberdeen.

Applicants should have a very good BSc (Honours) (First or Upper Second class) degree or a Master degree (with Distinction or Merit) in a related discipline.

Applicants should have good personal and communication skills, strong professionalism and integrity, and be capable of working on their own initiative.

Enquiries can be emailed to [email protected]  and will be forwarded to the lead academic if technical in nature.


Applications should be emailed to [email protected] by 31st July 2022, stating which studentship(s) you are applying for. You can apply for one or more.

The application should consist of:

  • A covering letter or personal statement of interest
  • CV
  • IELTS (or equivalent) certificate
  • Two references (at least one academic or professional).

Further information such as passport details or transcripts may be requested during the short-listing stage.

For more information regarding the University’s English language requirements please visit:

Please note that if response is high the application deadline may be brought forward; you are advised to apply as early as possible.

Ref: RGU2022DC01 – Neurosymbolic AI for Enhancing Personalised Summarisation of Regulatory Changes
Dr. David Corsar

Despite high-profile advances in use of machine learning (ML) for natural language processing (NLP), accurately summarising policy documents remains a challenging research problem. Ontologies and knowledge graphs (KG) are well established sources of domain knowledge that have been shown to improve NLP tasks such search and question answering. In this PhD project, you will investigate neurosymbolic Artificial Intelligence (AI), combining ML-based NLP and knowledge-based AI techniques to summarise regulatory updates relevant to an organisation’s operational activities. This will lay the foundations for an AI assistant that supports organisations to reduce the considerable resources currently invested in monitoring regulatory changes.

Ref: RGU2022KM01 – Machine Learning for Process Mapping of Multi-Investment Platforms
Dr. Kyle Martin

Multi-investment platforms allow financial experts to recommend multiple avenues for investment to customers. These platforms are multi-featured and tightly bound by strict regulation and legislation. Process mapping is a regulatory process to create a sequential map of every rule and dependency tied to each process in an investment platform. This requires substantial input from domain experts. We believe machine learning can streamline this endeavour, and highlight there is potential in Knowledge Graphs (KGs) and Case Based Planning (CBP). In this project, you will investigate KGs to represent process maps, allowing development of a CBP system for specific tasks such as process map validation and generation.

Ref: RGU2022MS01 – Conversation Structure Learning for Assistive Speech-to-Text
Dr. Mark Snaith

Understanding the structure of an unfolding conversation or dialogue is a fundamental part of spoken communication. It allows us not only to better follow the conversation, but also appreciate whose turn it is to speak. Through analysing past conversations, artificial intelligence can also be equipped with this understanding. Applying machine learning can expand this understanding to predicting future structure, such as expected types of utterance, and who will speak next. In this project, you will investigate machine learning techniques for predicting conversational structure, towards the development of enhanced assistive speech-to-text technologies aimed at the deaf and hard of hearing communities.

Ref: RGU2022EE01 – Deep Learning for Complex Documents and Engineering Diagrams
Professor Eyad Elyan

The overall aim of this project is to develop a Deep Learning framework for digitising engineering diagrams and complex documents. More specifically, the successful PhD candidate will lead the technical development and evaluation of new methods for processing and analysing engineering drawings. The challenge here involves the recognition of a large range of symbols and fixtures contained in digital drawings including the extraction of text and contextual information. The trained models will be used to optimise connectivity information within electrical diagrams for real-world tasks (e.g., route optimisation and wiring generation) in collaboration with our business partners.

Ref: RGU2022DC02 – Intelligent and multi-modal biometrics
Professor Eyad Elyan

The overall aim of this project is to develop new methods for detecting, analysing, and understanding of hidden patterns in human behaviour to predict their intentions or next movements in specific contexts. The successful applicant will build neural architectures, with emphasis on action recognition, human emotion analysis from facial images, and visual tracking to identify potential events.

A key focus involves comparative methods for training and evaluating multi-modal intelligent systems, which will be created and evaluated using public datasets as well as propriety data from the School of Computing partners.

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