Canterbury University invites applications for a *fully funded PhD position* at the School of Computing (ETD).
Title: Developing a machine learning predictive model and system for infectious disease. Short summary: A major element of personalized medicine involves the identification of therapeutic regimes that are safe and effective for specific individuals. It is a data-driven approach that relies on artificial intelligence and (big) multi-modal data from an individual to make patient-tailored decisions. The recent successes in using Artificial Intelligence/Machine Learning (AI/ML) across a wide range of difficult problems in various disciplines have inspired research in applying ML techniques to infectious disease prediction. Recent research in artificial intelligence models have reported some success in predicting some medical conditions such as heart disease, cancer and diabetes. The use of AI/ML in the field of infection management has been reported as being at its infancy. Currently, treatment decisions are not informed by data-driven models of patient risk for complications. Some existing machine learning clinical decision support systems including the ones developed for infection prediction still face a great number of challenges such as data quantity (including the number and the nature of features considered), data quality, model interpretability, evaluation in real world settings, deployability and sustainability. The objective of this PhD project is to investigate further whether infection caused by opportunistic pathogens can be predicted using machine learning. It also evaluates the impact of non-clinical data (socio-economic data/genomic data) on the machine learning predictive model. Research questions: • How can machine learning improve the prediction of infection occurrence? • What is the impact of non-clinical data (and features) on the predictive AI/ML model? • How can the results of AI/ML predictions be interpreted according to clinicians needs. Your experience and ambitions: We welcome candidates with a master’s degree (or international equivalent) in computer science, health informatics, or any relevant STEM field, who are curious about applied machine learning in biomedical sciences. Work experience may also be considered. We seek colleagues who enjoy coding, scripting and analytics, and who are keen to push the boundaries of machine learning and artificial intelligence. This project requires creative thinking and programming. Prior machine learning experience is a merit but not a requirement. We further appreciate willingness to travel, collaborate and communicate science. Ready to apply? For further information about this project, to apply for the position, please submit your application including the attachments mentioned below as one single PDF document in English to: Dr Scott Turner: scott.tur...@canterbury.ac.uk, Dr Leishi Zhang: leishi.zh...@canterbury.ac.uk, and Dr. Amina Souag: amina.so...@canterbury.ac.uk. (1) Letter of motivation. (2) CV (including list of publications, if any). (3) Contact details of at least two referees (or letters of recommendation, if already available). The position will be filled as soon as a suitable candidate is identified. About CCCU – Canterbury: The research of this project will be undertaken within School of Engineering, Technology, and Design (ETD) at Canterbury Christ Church University (CCCU). The project will be developed in collaboration with academics from Natural and Applied Sciences section at CCCU and East Kent Hospitals University NHS Foundation Trust. Canterbury Christ Church University is located in the world-famous Cathedral city amongst stunning history and heritage. Canterbury is a thriving international destination, with many students and staff choosing to study and work here, making this historic, cosmopolitan city vibrant and culturally diverse. We are strongly committed to equality and recognise the value of diverse students and staff. Dr Amina Souag Lecturer in computing School of Engineering, Technology and Design Canterbury Christ Church University | Canterbury | CT1 1QU
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