Funding

Self-funded

Project code

CMP10191026

Department

School of Computing, Mathematics and Physics

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, Mathematics and Physics and will be supervised by Dr Atefeh Khazaei.

 

This project explores efficient data selection strategies for text analysis and Natural Language Processing (NLP). Rather than relying on increasingly large datasets, the research investigates how smaller, carefully selected training datasets can achieve comparable or even superior model performance. The study will focus on identifying informative and representative textual samples that improve learning efficiency while reducing computational cost and redundancy.

The work on this project will:

  • Investigate whether smaller datasets can outperform larger datasets in text analysis tasks.
  • Explore modern data selection approaches such as:
    • Active Learning
    • Data Pruning
    • Curriculum Learning
    • Sample Selection and Filtering Techniques
  • Reduce training time and computational resource requirements.
  • Improve model efficiency while maintaining strong predictive performance.
  • Contribute to sustainable and scalable NLP research practices.

Large datasets in text analysis do not always guarantee better results. In many cases, excessive or redundant data can increase computational cost without significantly improving model accuracy. This project aims to develop optimal methods for reducing textual training data while preserving or enhancing model performance.

The research will examine techniques such as Active Learning, Data Pruning, Curriculum Learning, and other intelligent data selection methods to identify the most valuable training samples. By focusing on data quality rather than quantity, the project seeks to improve training efficiency, reduce redundancy, and create more scalable and resource-efficient NLP systems.

 

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 - eligibility criteria 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

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 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.

International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.

 

 

 

 

You will benefit from a background in text analysis and/or NLP.

 

 

How to apply

We’d encourage you to contact Dr Atefeh Khazaei ([email protected]) 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 CMP10580529.