Pharmacoepidemiology: Fully Funded PhD Studentship: Machine Learning for Pharmacoepidemiological Surveillance
The PhD student will develop data-driven methods, from electronic health data in Wales and France, to examine prescription patterns and develop signal detection models by machine learning and data mining techniques, regarding patient safety, new adverse events, known safety issues and possible unknown beneficial effects of drugs, which can be used to improve surveillance from routinely acquired electronic health data. This project uses asthma as an exemplar case, a growing chronic disease affecting around 10% of children in Europe for which international guidelines are established in this specific population. Asthma will be used as a case study in order to develop and test machine learning methods as a means of real time drug surveillance.
For enquiries, applicants are welcome to contact Prof Sinead Brophy and Dr Shang-Ming Zhou.
For applicants in France, you are also welcome to contact Prof Pierrick Bedouch and Dr Sebastien Chanoine.
The successful candidate is expected to start their PhD studentship in January.
Applicants should have a minimum of a 2.1 undergraduate degree and/or a Master's degree (or equivalent qualification) in Computer Science, Computing, Data Science, Statistics, Epidemiology, Health informatics, Medical Informatics, Bioinformatics or any other related areas.
The successful candidate should also have a good level of English and French (spoken and written). Applications from both the Université Grenoble Alpes and Swansea University are welcomed.
Due to funding restrictions, this studentship is open to UK/EU students only.
The studentship covers the full cost of UK/EU tuition fees, plus a tax free stipend (value to be confirmed).
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