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- Grade: UCL07 (see UCL Salary Grade)
- Institution: Institute of Health Informatics, UCL
- Location: London
- Term: 2 years
- How to apply:application submission link
- Contact: informal discussion with honghan.wu@ucl.ac.uk
The research fellow will conduct original health data science research within the KnowLab research group (https://knowlab.github.io/). This post is funded by NIHR Project titled: “Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC)”. The project aims to use AI methods to: Identify the most common combinations of long-term conditions that people have, and examine whether people inherit a tendency to get particular combinations of conditions from their parents. This will help us understand what causes multimorbidity, and identify potential treatments. The researcher will work closely with the AIM-CISC project team, which combines expertise in AI methods and clinical researchers with experience of delivering and researching healthcare for people with multiple long-term conditions. The researcher will be based at Insitute of Health Informatic, University College London.
The Research Fellow will conduct original research on the development and application of text analytics, knowledge driven methods and the combination of the two on health datasets with a focus on learning domain specific embeddings and comprehensive patient phenotyping. The aim is to extend the phenotyping potential of disease status algorithms and identifications of adverse events by combining additional linked datasets and unstructured data. The post holder will also help to create gold standard data for model validation, produce material for publication and dissemination and engage with project collaborators and support the delivery of a cutting-edge multidisciplinary programme of research.
| Criteria | Essential or Desirable | Assessment method (Application/Interview) | ||
|---|---|---|---|---|
| Qualifications, experience and knowledge | ||||
| PhD (awarded or imminent) in computer science, informatics or a related discipline (e.g., natural language processing, artificial intelligence, machine learning), or equivalent extensive experience in those areas | Essential | A | ||
| Knowledge and Experience with natural language processing techniques on solving real world problems and large-scale datasets | Essential | A & I | ||
| Knowledge and Experience with ontology and knowledge driven technologies, particularly ontologies and open datasets in medical or chemistry domains | Essential | A & I | ||
| Experiences of analysing and mining large-scale clinical free-text data | Essential | A & I | ||
| Experiences of applications of AI methods in improving health care delivery | Desirable | A & I | ||
| Experiences of working in multidisciplinary teams including clinicians or clinical researchers | Desirable | A & I | ||
| Top-tier peer-reviewed publications in natural language processing and/or machine learning | Desirable | A & I | ||
| Training and experiences in deep neural-network based algorithms | Desirable | A & I | ||
| Training in safe data handling and GDPR | Desirable | A & I | ||
| Skills and abilities | ||||
| Strong programming skills, preferably with experience in applying machine learning to text mining | Essential | A & I | ||
| Experience of writing scientific papers and reports | Essential | A & I | ||
| Excellent oral and written communication skills | Essential | A & I | ||
| Ability to work flexibly in a large multi-disciplinary team | Essential | A & I | ||
| Experience text data annotation | Desirable | A & I | ||
| Personal attributes | ||||
| Personal motivation and enthusiasm for the projects | Essential | A & I | ||
| Excellent organisational and time-management skills | Essential | A & I | ||
| Attention to detail and commitment to scholarship | Essential | A & I | ||