Fully funded PhD Studentship – Automating systematic literature reviews with machine learning

Fully funded PhD Studentship

Automating systematic literature reviews with machine learning

Robert Gordon University

Closing Date – Midnight, Sunday, 6 June 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 Carlos Moreno-Garcia and Professor Nirmalie Wiratunga, with co-supervision from Dr Magaly Aceves-Martins (The Rowett Institute, University of Aberdeen, Cochrane Collaboration Member).

Duration and Funding

The duration of the project will be up to 36 months, commencing in 2021. The studentship covers both tuition fees (at UK or International level) and a tax-free stipend (living allowance) of £15,000 per annum.

The successful candidate will be required to relocate to Aberdeen as soon as possible to study, although studies may start remotely initially.

Proposed Research

The proposed research is aimed at investigating the most effective machine learning-based solutions that can optimise the evidence-based synthesis process in health sciences (e.g., systematic reviews or rapid reviews).

Synthesising facts and figures from a vast number of literature documents containing a mix of text, images, tables and references is a daunting task involving significant human hours. However, accessing key concepts, relationships and linking these to domain ontologies, references and tabular data, if automated can significantly reduce the manual burden on researchers.

The aim of this research is to investigate how recent advances in natural language processing and image recognition methods can be harnessed to automate the synthesis of academic literature.

Specifically we expect to improve aspects of the evidence synthesis currently done in manual ways that are time-consuming and increase the risk of missing data. Areas that we are looking to improve include literature identification, understanding the relations between author/topics/data, PICOS (Population, Intervention, Comparator, Outcomes and Study Design) items identification, analysis and synthesis (narrative and meta-analysis). In addition, predictive analytic models will be implemented to understand the relevant literature’s data trends.

The novel machine learning methods developed in this project will be evaluated on systematic literature reviews in the healthcare domain. We will also use findings from here to strengthen our links with the COMO project, which is a multinational and multidisciplinary effort aimed at tackling childhood obesity in Mexico.

This project is funded by the Robert Gordon University and will involve collaborators from institutions such as the University of Aberdeen, Tec de Monterrey, UNICEF and The Data Lab Scotland.

Key Skills

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

Essential Knowledge and Experience:

  • IELTS (or equivalent) at 6.5 overall and 6.5 in each component/element is required for Research based degrees – the successful candidate will be required to pass the IELTS or equivalent English qualification before starting their studies. For more information, please visit https://www.rgu.ac.uk/study/international-students/english-language-requirements
  • Strong programming skill in Python or similar languages.
  • Knowledge of machine learning packages (Tensorflow/Keras, NLTK or similar).
  • Solid basis and understanding of statistics.

Desirable requirements

  • Experience with deep learning, natural language processing and information extraction.
  • Experience with multiple modal data processing e.g. text, images, tabular.
  • Experience or knowledge in evidence synthesis for health sciences.
  • Knowledge of statistical analysis.
  • Applicants should have good personal and communication skills, have a strong professional approach to work, respect high degree of integrity at work, and be willing to work on their own initiative.

Enquiries

Enquiries can be submitted through https://www.findaphd.com/phds/project/automating-systematic-literature-reviews-with-machine-learning/?p127923 or be emailed to Kate Lines at soc-researchadmin@rgu.ac.uk and will be forwarded to Dr Carlos Moreno-Garcia if technical in nature.

Applications

Applications should be submitted through https://www.findaphd.com/phds/project/automating-systematic-literature-reviews-with-machine-learning/?p127923 or be emailed direct to Kate Lines at soc-researchadmin@rgu.ac.uk by midnight, Sunday, 6 June.

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. Interviews (which may include a short practical test) are expected to take place week commencing 5 July 2021.