About the Project
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 Alaa Mohasseb.
Project Highlights:
The work on this project will:
- Develop computational approaches for detecting and mitigating bias in large language models.
- Investigate explainable and interpretable AI techniques to improve transparency and trust in NLP systems.
- Design evaluation frameworks for assessing fairness, robustness, transparency, and reliability in generative AI.
- Explore fine-tuning, adaptation, and benchmarking strategies for responsible and human-centered language models.
Project description:
Artificial Intelligence (AI) and Natural Language Processing (NLP) are increasingly embedded in systems that shape communication, knowledge production, and decision-making. The rapid advancement of large language models and generative AI has accelerated this transformation, enabling technologies capable of generating, interpreting, and influencing human language at unprecedented scale. While these developments create significant opportunities across domains such as healthcare, education, media, and governance, they also raise important challenges related to bias, misinformation, accountability, transparency, and unequal access. Addressing these challenges requires not only critical reflection, but also the development of technical methods that support more trustworthy, fair, and human-centered AI systems.
This project aims to develop and evaluate computational approaches for advancing responsible and human-centered NLP. The research may explore methods for bias detection and mitigation in large language models, investigate interpretability and explainability techniques for understanding model behaviour, and design evaluation frameworks for assessing fairness, robustness, transparency, and reliability across diverse contexts. The project may also involve the fine-tuning, benchmarking, and evaluation of pre-trained language models and generative AI systems.
The proposed research will contribute to the growing field of responsible AI by advancing both the technical capabilities and societal understanding of NLP systems. The developed methods and evaluation frameworks are expected to support the design of more transparent, reliable, and human-centered AI technologies across a range of real-world applications in academia and industry.
General admissions criteria
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.
Specific candidate requirements
A good background in Natural Language Processing, Machine Learning, and Deep Learning is desirable, with familiarity with large language models and generative AI considered advantageous. Experience with NLP frameworks and libraries such as NLTK, spaCy, or Hugging Face Transformers, and machine learning tools such as Scikit-Learn, PyTorch, or TensorFlow, would be beneficial. Familiarity with fairness, interpretability, or evaluation frameworks and libraries (e.g., Fairlearn, SHAP, LIME) is also desirable.
Strong programming skills in Python, good analytical and problem-solving abilities, and a solid foundation in computer science are expected. The project will involve independent research, including the development of research questions and ideas, alongside strong written and verbal communication skills and effective time management and organisational abilities.
How to Apply
We'd encourage you to contact Dr Alaa Mohasseb [contact details available in the full listing] 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 CMP10610529.
Funding Notes
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.
