Self-adaptive Machine Learning Methods For Outcome Prediction In Real-world Complex Datasets

PhDs and postgraduate research

Funding

Self-funded PhD students only

Project code

CCTS4600219

Department

School of Computing

Start dates

February and October

Closing date

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 2020 or February 2021. The PhD will be based in the School of Computing and will be supervised by Dr Mohamed Bader and Dr Bryan Carpenter.

Overview

This 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.

Fees and funding

Funding availability: Self-funded PhD students only.

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

2020/2021 entry (for October 2020 and February 2021 entries)

Home/EU/CI full-time students: £4,407 p/a

Home/EU/CI part-time students: £2,204 p/a

International full-time students: £16,400 p/a

International part-time students: £8,200 p/a

PhD by Publication

External candidates £4,407 p/a

Members of staff £1,680 p/a*

2021/2022 entry (for October 2021 and February 2022 entries)

PhD and MPhil

Home/EU/CI full-time students: £4,407 p/a*

Home/EU/CI part-time students: £2,204 p/a*

International full-time students: £17,600 p/a

International part-time students: £8,800 p/a

All fees are subject to annual increase.

PhD by Publication

External Candidates £4,407 p/a*

Members of Staff £1,720 p/a*

If you are an EU student starting a programme in 2021/22 please visit this page.

*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

General admissions

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.

Specific admissions

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.