Fully-funded PhD Studentship – Conversational Artificial Intelligence for Digital Healthcare Triaging, Self-Management and Interventions

Fully-funded PhD Studentship

Conversational Artificial Intelligence for Digital Healthcare Triaging, Self-Management and Interventions

Robert Gordon University

Closing Date: Midnight, 7 January 2021

Applications are sought for a fully funded Research Studentship (PhD) to carry out research at Robert Gordon University, Aberdeen, United Kingdom, under the supervision of Dr Ikechukwu Nkisi-Orji and Professor Nirmalie Wiratunga with co-supervision from their team.

Duration and Funding

The duration of project will be up to 36 months, ideally commencing 1 February 2021 or as circumstances allow. The studentship covers both tuition fees (at Home / EU or International level) and a tax-free stipend of £15,000 per annum.

Proposed Research

Artificial Intelligence (AI) now plays an increasingly crucial role in self-management, to provide low-cost solutions for personal health which enhance healthcare outcomes and reduce the burden on healthcare services. AI for triaging promises to significantly reduce the demand for human assessment resources and optimise the classification and prioritisation of patients.


The proposed research aims to investigate the role of conversational chatbots in fulfilling the requirements for healthcare triaging and follow-on personalised interventions for self-management. One of the main AI technologies for this purpose is conversational AI, a form of human-computer interaction using natural language interfaces. Adoption of this technology can range from automated messaging and speech-enabled interfaces that allow people to interact with applications, to websites, and devices using voice, text, touch, or gesture information and to personal assistants like Alexa. In healthcare applications, voice assistants promise increased engagement, improved patient trust, and in addition, encourages patient adherence and influences positive health outcomes. However, generating meaningful responses that are contextually relevant remains a challenge for conversational AI. More specifically, topics such as natural language understanding, natural language generation, data representation schemes (XML/AIML, ontologies) and case-based reasoning will be explored to model conversations and improve the quality of AI dialogue within a multi-strategy response generation framework.


During the research study, the student will work with the industrial partner PhysioMedics and leverage the existing expertise and collaboration between the academic and industrial partner in this research area. PhysioMedics owns the PhysioWizard™ platform which replicates the face-to-face clinical triage carried out by healthcare professionals by empowering patients to self-assess their musculoskeletal conditions and rapidly transition them to the recommended pathway of care. The use of conversational chatbots for triaging will broaden delivery methods and provide richer experiences that will facilitate the ease of adoption across demographics and promote more equitable service delivery.

Key Skills

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


Essential Knowledge and Experience:

  • Strong programming skill in Python/Java or similar languages.
  • Knowledge of machine learning packages (Tensorflow/Keras, NLTK or similar).
  • Good analytical skills – knowledge of foundations of computer science, ability to think independently.
  • Strong oral and written communication skills, in both plain English and academic languages, for publication in relevant journals and presentation at conferences.


Desirable requirements

  • Experience or knowledge in evidence synthesis for health sciences.
  • Experience with deep learning, natural language processing and information extraction.
  • Knowledge of XML and ontological knowledge resources.
  • Mixed-method evaluation including knowledge of statistical tools (R/MatLab or similar).

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 Kate Lines at soc-researchadmin@rgu.ac.uk and will be forwarded to Dr Ikechukwu Nkisi-Orji if technical in nature.



Applications should be emailed to Kate Lines at soc-researchadmin@rgu.ac.uk by 7 January 2021. The applications should consist of a covering letter or personal statement of interest, and a CV. Further information such as passport details or transcripts may be requested during the short-listing stage. Interviews (which may include a short practical test) are expected to take place week commencing 19 January 2021.