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Source-code similarity detection using deep learning

  • Full or part time
  • Application Deadline
    Tuesday, September 01, 2020
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Deep learning is a subset of machine learning in artificial intelligence (AI) that is capable of learning from data.

Deep learning is receiving a lot of attention due to its ability to achieve unprecedented levels of performance in terms of accuracy and speed, to the point where deep learning algorithms can outperform humans at decision making, and at tasks such as classifying images, and text.

Deep Learning is been utilised for various natural language processing (NLP) applications. NLP concerns the development of computational algorithms to automatically analyse and represent human language. However, the task of utilising deep learning methods for analysing source-code (e.g. programs written in the Java, and C# programming languages) has not been exploited as much.

This project involves the development of deep learning models for analysing, classifying and clustering files in large source-code for indexing and similarity detection tasks.

If you are interested please get in touch with Dr Cosma () to discuss the topic further.

You can find out more here: https://datascienceplus.blog

How to apply

All applications are made online, please select the school/department name under the programme name section and include the quote reference number.

https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/

Entry Requirements

A relevant Master's degree and / or experience in one or more of the following will be an advantage: data analytics, artificial intelligence and machine learning. A background in metaheuristic optimisation methods and/or intelligent information retrieval is desirable.

Funding Notes

This is an open call for candidates who are sponsored or who have their own funding. If you do not have funding, you may still apply, however Institutional funding is not guaranteed. Outstanding candidates (UK/EU/International) without funding will be considered for funding opportunities which may become available in the School.

UK/EU Fee band: Research Band 2 Laboratory Based (£tbc)
International Fee band: Research Band 2 Laboratory Based (£22,500)

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