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 could involve:
- data analytics and preprocessing of datasets (investigating and applying techniques for dealing with missing, incomplete, imbalanced, noised, shifted data, etc. datasets);
- investigation, analysis, and design of Deep Learning 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 methods 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 (specifically in health informatics and healthcare area).
Project description
In the 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 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 the Deep Learning methodologies, will have to investigate DL approaches and algorithms, design 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 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, 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 (more specifically, for gaining insights from big datasets collected during fetal monitoring at labour, for achieving objective assessment and reducing the risk of new-born babies’ asphyxiation and brain damage, when inferring and predicting the labour outcome (as part of ongoing 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).
General admissions criteria
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.
Specific candidate requirements
The ideal candidate for this PhD will have BSc and MSc in computer science and related areas. Some background in machine learning, knowledge engineering and having interest in data analytics, image and signal processing, and deep neural networks 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 both on fundamental and application aspects of this research.
How to Apply
We encourage you to contact Dr Ivan Jordanov ([Email Address Removed]) to discuss your interest before you apply, quoting the project code below.
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:COMP5901023
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