*Application closing date has been extended for this project - Interviews will be held on Friday 11 March 2022*
*CLOSING DATE: 3 MARCH 2022*
A key step for planning health interventions in LMICs is mapping the evidence on unmet needs. Systematic reviews (SRs) are commonly used for this purpose, but they are resource-intensive. An increasing amount of research has shown that some steps of SRs can be automated with Artificial Intelligence (AI). We aim to investigate the use of self-supervised artificial intelligence to search for evidence on unmet needs and targets for potential new interventions to improve the quality of life of people with vision impairment in LMIC.
Collaboration is a fundamental part of research achievements. The researcher will be part of a large team including global health and epidemiology experts together with librarians and artificial intelligence scientists. As part of an ongoing collaboration with SANO (Centre for Computational Personalised Medicine) https://sano.science, there is the possibility to join a bigger effort to explore AI unique capabilities within a research centre in central Europe and stay there for 6 months.
The proposed framework is built using self-supervised learning models with the ability to learn from semantic representations of data, allowing an integrated knowledge representation of different data types while evolving its structure to different data sources. The training dataset will include existing eye Global Health reviews, including studies selected by human experts. Self-supervised knowledge prototyping using complex networks meta-modelling will be adopted. Computational network analysis of the titles and abstracts of studies included in the systematic review will be performed. Finally, results from the AI-led and the human-led processes of extracting data will be compared.
Funding eligibility: ROI (and EU applicants with pre-settled/settled) status may be eligible for funding. Please review the DfE T&C’s.
ENTRY REQUIREMENTS
You must hold or expect to get an upper second class honours degree from a university in the UK or Ireland, or qualifications and experience considered by the University as equivalent to that standard. Candidates who already hold a doctoral degree, or who have registered on a PhD for one year (or part-time equivalent) or not eligible.
English Language
Candidates applying from countries where the first language is not English should produce evidence of their competence through a qualification such as IELTS or TOEFL score.
The minimum recommended score for the School of Medicine, Dentistry and Biomedical Science is:
· IELTS score of 6.0 with not less than 5.5 in each of the four component elements of listening, reading, speaking and writing taken within the last 2 years;
· TOEFL score of 80+ (internet basted test), taken within the last 2 years, with minimum component scores of; Listening 17, Reading 18, Speaking 20, Writing 17);
· A valid Certificate of Proficiency in English grade A or B;
· A valid Certificate of Advanced English grade A; or
· A first or upper second class honours degree from a university based in the UK, Republic of Ireland or other suitably quality assured location in a country deemed by the UK Border Agency to be majority English speaking.
For a list of English Language qualifications also accepted by the School and University please see the following link:
http://www.qub.ac.uk/International/International-students/Applying/English-language-requirements/#English
INTO Queen’s English Language Courses offers both pre-sessional and in-sessional courses in English for academic purposes and study skills. Courses vary in length and full information can be obtained at: https://www.qub.ac.uk/International/International-students/Applying/University-Preparation-Courses/INTOEnglishlanguagecoursesatQueens/
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