PhD Studentship: Adaptive Large Language Models for Low-Resource Languages: Bridging the Global Digital Language Divide

This PhD asks a different question: instead of demanding more data, can we build language models that learn smarter from less? You will design AI architectures that adapt to the structure of a language, including its grammar, script and complexity, rather than to how much text happens to be available online. You will test these ideas across a diverse range of underserved languages, and work directly with speaker communities to ensure the technology is genuinely useful to them.

You will gain deep expertise in machine learning and natural language processing, access to high-performance computing facilities, and support to publish at leading international conferences. You will join a supportive, collaborative research community with structured training, funded conference travel, and opportunities to build a strong academic or industry career.

Objectives

This project develops and evaluates modular, adaptive LLM architectures that allocate capacity according to linguistic complexity rather than corpus size, enabling accurate, robust and culturally appropriate technology for low-resource languages. The objectives include: 

• Establish reproducible testbeds across typologically diverse low-resource languages. 
• Diagnose how existing adaptation mechanisms balance cross-lingual transfer against language-specific fidelity, using benchmarks such as FLORES-200 and MasakhaNER. 
• Design complexity-aware allocation mechanisms which condition capacity on typological features rather than data volume.
• Evaluate it against strong multilingual baselines, with ablations testing whether it mitigates the curse of multilingualism.
• Co-develop data curation and evaluation protocols with native speakers, measuring gains in cultural appropriateness and robustness beyond automatic metrics. 

Funding

These are doctoral teaching assistant positions that combine a PhD programme with a university teaching contract. Your time will be split approximately 60% on research and 40% on teaching. This provides excellent preparation for candidates considering an academic career at a university. The teaching component will typically run over the 22 teaching weeks per year and the four assessment weeks. You will help deliver an outstanding student experience by supporting lead academics with classroom and lab teaching and assessment, further building the skills developed within your PhD research programme.

The position is grade 6 with a current salary of £31,236 and includes payment of home PhD tuition fees for the duration of the 6-year award. Home students can apply. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role.

Candidate requirements

Essential

• Skills in programming, preferably in Python, and a suitable undergraduate / masters degree in the discipline of Computer Science, NLP, AI or related fields. 
• Right to work in the UK. Visa sponsorship is not available for these roles, and only home PhD fees will be paid. 

How to apply

If you have any questions, contact the principal supervisor, Dr Seun Ajao.

To apply you will need to complete the online application form for a part time PhD in Computing and Digital Technology.

Please complete the Doctoral Project Applicant Form, and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.

Please upload these documents in the supporting documents section of the University’s Admissions Portal or send them to the PGR Admissions team at pgradmissions@mmu.ac.uk.

Please quote the reference: SciEng-DTA Jan 2027-SA-Low-Resource LLMs