(Fully-funded) PhD Studentship – Machine Learning to Improve the Systematic Review Pipeline

(Fully-funded) PhD Studentship

Machine Learning to Improve the Systematic Review Pipeline

Robert Gordon University

Closing Date: 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 Carlos Moreno-Garcia and Professor Nirmalie Wiratunga with co-supervision from Dr Magaly Aceves-Martins (The Rowett Institute, University of Aberdeen).

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

The proposed research is aimed at investigating the most effective algorithms, techniques and machine learning based solutions that can assess the systematic review pipeline. More specifically, topics such as document image analysis, optical character recognition, natural language processing, data mining, statistical analysis and graph representations (amongst others) will be explored to improve processes such as locating the literature and sources of interest, identifying relations between author/topics/data, and most importantly, automatically screening studies to assist in the time-consuming process of inclusion and exclusion of relevant outcomes. In addition, predictive analytic models will be implemented to understand the data trends contained in the relevant literature.

This research will be done as part of the COMO project, which is a multinational, multidisciplinary effort aimed at tackling childhood obesity in Mexico. This project has the approval and collaborators from institutions such as UNICEF, University of Aberdeen, Tec de Monterrey and The Data Lab Scotland.

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, Biostatistics or related discipline.

Essential Knowledge and Experience:

  • Strong programming skill in R/Python/Matlab or similar languages.
  • Knowledge of machine learning packages (Tensorflow/Keras, NLTK or similar).
  • Solid basis and understanding of statistics.

Desirable requirements

  • Experience or knowledge in evidence synthesis for health sciences.
  • Experience with deep learning, natural language processing and information extraction.
  • Knowledge of statistical tools (SPSS/Stata/RevMan/Comprehensive MetaAnalysis or similar).
  • Knowledge of Spanish would facilitate interaction with stakeholders.

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

Enquiries

Enquiries can 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 emailed to Kate Lines at soc-researchadmin@rgu.ac.uk by 7 January 2021. The application 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.