Deep Learning for Pattern Recognition, Identification & Classification from Big 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 February or October 2020.
The PhD will be based in the School of Computing and will be supervised by Dr. Ivan Jordanov and Dr. Antoniya Georgieva (University of Oxford).
With the current advances of computer processing and memory power and developments of Internet of Things the abundance of collected data is becoming reality in almost every area of human activity (machine and man generated data, as sensors, crowdsourcing, social media, and other sources generated data). Once the data is collected, the need and role of Machine Learning methods for extracting valuable information, relationships, and trends is becoming more and more important.
In the recent years, Deep Learning (DL) has demonstrated remarkable success in solving image, pattern, and especially in speech recognition problems. 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 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 data analytics problems associated with preprocessing of most real world dataset - related to the data quality (e.g., how to deal with missing, incomplete, imbalanced, noised, and shifted data).
You will also have the chance to train, test, and evaluate your deep learning approaches and neural network architectures on real world datasets gaining insights from big datasets collected during foetal monitoring at labour achieving objective assessment and reducing the risk of new-born babies’ asphyxiation and brain damage (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).
PhD full-time and part-time courses are eligible for the Government Doctoral Loan (UK and EU students only).
- Full-time students: £4,327 p/a*
- Part-time students: £2,164 p/a*
- Full-time students: £15,900 p/a*
- Part-time students: £7,950 p/a*
By Publication Fees 2019/2020
- Members of staff: £1,610 p/a*
- External candidates: £4,327 p/a*
*All 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 a relevant subject 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
- Candidates are required to have MSc/BSc degree in computer science, artificial intelligence, mathematics, physics, or similar areas. MSc students that expect to graduate soon may also apply.
- Substantial programming skills, including working knowledge of at least one of the C++, Python, Java programming languages will be an advantage.
- Some experience with the design and implementation of neural networks and learning algorithms is desirable, but not a strict requirement.
- Candidates should be interested in working on both fundamental and applied research collaborating with experts in cross-disciplinary areas (computational intelligence, data analytics, health informatics and bioinformatics).
How to apply
We’d encourage you to contact Dr. Ivan Jordanov (email@example.com) to discuss your interest before you apply, quoting the project code CCTS4490219.
When you're ready to apply, you can use our online application form and select ‘Computing’ 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 offers further guidance on the PhD application process.