Self-adaptive Machine Learning Methods For Outcome Prediction In Real-world Complex Datasets
Self-funded PhD students only
School of Computing
Applications accepted all year round
Applications are invited for a self-funded, 3-year full-time or 6-year part-time PhD project, to commence in October 2019 or February 2020. The PhD will be based in the School of Computing and will be supervised by Dr Mohamed Bader and Dr Bryan Carpenter.
OverviewThis project will focus on the development of novel machine learning methods for outcome prediction in real-world complex application. The data-sets required in our target areas are very large, and we are investigating the application of techniques from High Performance Computing as enablers in applying machine learning techniques to them. The project will be based on recent work will focus on one more of the following predefined applications:
1. Early outcome prediction in healthcare
In this application the focus will be on MIMIC and eICU database which contains more than 3 million ICU stays and billions of medical measurements that is collected from more than 400 hospitals. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed. However, most of the these methods are static not are not capable of dealing with size and speed of the current available data, therefore there is huge need for new and novel Big Data in ICU. The student will join an existing team working on risk and mortality prediction in ICU. The team has access to an existing preprocessed ICU database that is compatible with several data mining and data analytics tools.
2. Consumer behaviour prediction for digital marketing
This part will take place in collaboration with a leading digital marketing which offers a real-time personalisation and automation across email and web that optimises revenue for e-commerce companies. Its Personalisation Platform enables digital marketers to engage shoppers in contextual interactions to improve the shopping experience and, ultimately, close more sales. However, there is still a need for developing machine learning methods capable of predicting consumer behaviour and predicting the best marketing strategies for different consumer behaviour and groups.
PhD full-time and part-time courses are eligible for the Government Doctoral Loan (UK and EU students only).
Home/EU/CI full-time students: £4,327 p/a*
Home/EU/CI part-time students: £2,164 p/a*
International full-time students: £15,900 p/a*
International part-time students: £7,950 p/a*
*Fees are subject to annual increase
By Publication Fees 2019/2020
Members of staff: £1,610 p/a*
External candidates: £4,327 p/a*
*Fees are subject to annual increase
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an Civil Engineering or related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
The successful candidate will have:
A minimum of an upper second-class classification (or equivalent) in Computer Science, Mathematics or Health Informatics
Strong programming skills, Python and R are preferred
Solid machine learning and data mining skills
Excellent problem solving
Experience in one or more of the following areas is highly desired: Deep learning, Computational Intelligence, Data Mining, Machine learning, Health Informatics and/or Data Analytics
How to apply
We’d encourage you to contact Dr Mohamed Bader (Mohamed.Bader@port.ac.uk) to discuss your interest before you apply, quoting the project code CCTS4600219.
When you're ready to apply, you can use our online application form and select ‘Computing and Creative Technologies’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Our How to Apply page also offers further guidance on the PhD application process.