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PhD Studentship - Automating Medical Concept Extraction and Relevant Feature Annotation from Medical Literature using Machine Learning

   Population Health Sciences Institute

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  Dr Jawad Sadek  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Number of awards


Start date and duration

September 2021 for 3 years full time.


We are seeking a highly motivated PhD student to apply and develop novel machine learning methods to provide intelligence insights on life sciences innovation. The student will be based in the NIHR Innovation Observatory at Newcastle University, but will work collaboratively across the School of Computing and the Population Health Sciences Institute (PHSI). PHSI is the academic home of the Innovation Observatory.

The aim of this research project is to develop machine learning and natural language processing approaches to automatically extract relevant features from the biomedical literature and open source data. This will likely involve using pattern recognition techniques to discover biomedical terms, and word embedding techniques (e.g. Word2Vec, GloVe) that represent words within the corpora of text in low dimensional vectors, which helps in capturing similarity and relatedness between clinical terms. The project will also involve integrating cloud computing services that can scale to handle the demands of processing large volumes of unstructured text. Some of these elements will build from existing Innovation Observatory tools and systems.

This project will also investigate different ways of linking various medical sources (clinical registries, bio-samples, unpublished reports, patents, etc.) and storing them in a structured repository of textual data.

The project provides an excellent opportunity to apply artificial intelligence to the critically important and growing area of biomedical research. The candidate will have the option to choose the research trajectory within the broad strokes of the project and will receive support from experienced academics with expertise across various areas of computing, machine learning and medical sciences.

The project is suitable for candidates from Computing Science and/or Maths and Stats backgrounds who are interested in deep learning, text mining, natural language processing and programming in Python. Familiarity with a deep learning framework, such as PyTorch and TensorFlow, is desirable but not necessary as there will be an opportunity for the candidate to gain competency in the area.


Newcastle University

Name of supervisor(s)

Dr Jawad Sadek, Dr Amir Atapour-Abarghouei, Professor Dawn Craig.

Eligibility Criteria

The successful applicant will have, or expect to obtain an upper second class or above in Computer Science/Maths/Statistics (or a cognate discipline). A Masters degree (Merit or above) in a similar subject is desirable but not essential. If your first language is not English you need an overall IELTS score of 6.5 (at least 5.5 in all sub-skills) or equivalent language qualification. Non-UK candidates must contact [Email Address Removed] regarding eligibility and fees.

How to apply

You must apply through the University’s online postgraduate application system by creating an account. To do this please select ‘How to Apply’ and choose the ‘Apply now’ button.

All relevant fields should be completed, but fields marked with a red asterisk must be completed. The following information will help us to process your application. You should:

  • click on programme of study
  • insert 8300F in the programme code section and click search
  • select Programme name ‘PhD in the Faculty of Medical Sciences (full time) – Health Services Research’
  • insert PH025 in the studentship/partnership reference field
  • attach a covering letter and CV. The covering letter must state the title of the studentship. Please quote reference code PH025 and state in the covering letter how your interests and experience relate to the proposed project
  • attach degree transcripts* and certificates and, if English is not your first language, a copy of your English language qualification.

*You will not be able to submit your application until you have submitted your degree transcript/s.

Funding Notes

100% of home tuition fees paid and annual living expenses at UKRI rate (currently £15,609 per annum). Successful international students will be required to make up the difference (C. £17K per year) between the home and international fees. A research training support grant of £5000 per year is available to cover research costs and local/national/international travel (conferences and exchanges).
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