Project code



School of Computing

Start dates

February and October

Closing date

Applications accepted all year round

Applications are invited for a three year PhD to commence in October 2020 or February 2021.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr. Ivan Jordanov and Prof. Raymond Lee.

The work on this project could involve:

  • data analytics and preprocessing of datasets (investigating and applying techniques for dealing with missing, incomplete, imbalanced, noised, and shifted data, etc. datasets);
  • investigation, analysis, and design of DL approaches and algorithms, and proposing suitable topologies and architectures of convolutional neural networks (CNN) and long-short term memory neural networks (LSTM) for solving pattern recognition, classification, and signal processing problems;
  • designing learning and training strategies for the adopted deep learning approaches and the employed neural network architectures;
  • simulation, testing, and evaluation (analysis and adoption of performance metrics) of the developed deep learning architectures on real world datasets.

Investigating and applying Deep Learning approaches for solving image, object, and signal recognition and classification problems. Analysis and design of efficient convolutional neural network (CNN) and long-short term memory neural network (LSTM) topologies and architectures and proposing learning/training, testing, and evaluation strategies for the adopted deep learning approaches and the employed neural network architectures on real world datasets.

In the recent years, Deep Learning (DL) has demonstrated remarkable success in solving image, object, and especially in speech and signal recognition systems. The current advancements of the deep learning approaches (DLA) provided evidence that on big data, sophisticated algorithms can achieve better performance than simple models (the traditional shallow learning methods). The DL ability to learn feature hierarchies with multiple levels of abstraction allows the system to learn complex functions while mapping the input to the output directly from data, without the need and complete dependency on man-crafted feature and high level concept extraction.

In your research you will aim at getting better and in-depth understanding of the working mechanisms behind the success of Deep Learning and will have to investigate DL approaches and algorithms, and propose suitable topologies and architectures for convolutional neural networks (CNN) when solving pattern recognition and classification problems and long-short term memory neural networks (LSTM) when dealing with time series and signal processing problems. You will also have to investigate and work on associated preprocessing data analytics problems, typical for most real world datasets and related to the data quality (e.g., how to deal with missing, incomplete, imbalanced, noised, shifted, etc. data).

The investigation will include designing learning/training, testing, and evaluation strategies for your deep learning approaches and the employed neural network architectures on real world datasets. For example, the research will lead to applying the developed system for gaining insights from big datasets collected during fetal monitoring at labor, achieving objective assessment and reducing the risk of new-born babies’ asphyxiation and brain damage, when inferring and predicting the labor outcome (as part of ongoing academic research collaboration with Oxford Centre for Fetal Monitoring Technologies, Nuffield Department of Women's and Reproductive Health, and The Big Data Institute, University of Oxford)

Fees and funding

Visit the research subject area page for fees and funding information for this project.

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).

Entry requirements

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. 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 ideal candidate for this PhD will have BSc and MSc in computer science and related areas. Some background in machine learning and knowledge engineering and having interest in data analytics, image and signal processing, and deep neural networks. Working knowledge of at least one of the following programming languages: Python, C++, or Java is preferable and potential candidates should have a clear interest in working both on fundamental and application aspects of this research.

How to apply

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. 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 offers further guidance on the PhD application process.