Project code



School of Computing

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Ivan Jordanov.


The work on this project will:

  • Data analytics and preprocessing of datasets (investigating and applying techniques for dealing with noise, missing, incomplete, imbalanced, shifted, etc. datasets);
  • Investigation, analysis, and design of Deep Learning approaches and algorithms, and proposing suitable topologies and architectures of convolutional neural networks (CNN), long-short term memory neural networks (LSTM), and transformer networks for solving identification, classification, recognition, and signal processing problems;
  • Designing learning and training strategies for the adopted deep learning approaches and the employed neural network architectures;
  • Simulation, testing, evaluation, validation and ablation (analysis and adoption of performance metrics) of the developed deep learning models on real world datasets (specifically in health informatics and healthcare areas). 


In recent years, Deep Learning (DL) has demonstrated remarkable success in solving problems from image, object, and especially in speech and signal recognition systems. The current advancements of the DL approaches (e.g., generative AI large language models), evidence that on big data and large multimodal datasets, sophisticated algorithms can achieve results comparable with the human intelligence (and in some cases even to outperforming it).

In your research you will aim at getting better and in-depth understanding of the working mechanisms behind the success of the Deep Learning methodologies, will have to investigate DL approaches and algorithms, design and propose suitable neural network architectures and topologies when solving pattern recognition and classification problems (including time series and signal processing problems). You will also have to investigate and work on associated data analytics problems, typical pre-processing needed for most real world datasets, and related to the data quality (e.g., dealing with missing, incomplete, imbalanced, noisy, shifted, etc. data).

The investigation will include designing learning/training, testing, evaluation, validation and ablation strategies for your deep learning approaches and the employed neural network architectures on real world datasets (from health, health monitoring, and healthcare multimodal datasets).

Expertise of the supervisory team: 

Dr Ivan Jordanov’s expertise is in AI/Machine Learning/Neural Networks and Data Analytics. He has been PI and CoPI of a number of research projects (funded by EPSRC, KTP, EU COST, UNIDO, EU-TEMPUS, Royal Society, and others), he has published more than 100 papers and supervised to a successful completion more than 20 PhD students.


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 (conditions apply).

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

The entry requirements for a PhD or MPhil include an upper second class honours degree or equivalent in a relevant subject or a master's degree in an appropriate subject. Exceptionally, equivalent professional experience and/or qualifications will be considered. All applicants are subject to interview.

If English is not your first language, you'll need English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

If you don't meet the English language requirements yet, you can achieve the level you need by successfully completing a pre-sessional English programme before you start your course.

The ideal candidate will have BSc and/or MSc in computer science and related areas. Some background in machine learning, knowledge engineering, and having interest in data analytics, would be beneficial. Working knowledge of at least one of the following programming languages: Python, Java, or C++, is preferable and potential candidates should have a clear interest in working on both fundamental and application aspects of this research.


How to apply

We’d encourage you to contact Dr Ivan Jordanov ( to discuss your interest before you apply, quoting the project code.

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. 

When applying please quote project code: COMP6311025