PhD Studentship Opportunity: TRUST: Transparent, Responsible, User-centred Sexual-health Technologies using Natural Language Processing

Supervisors: Dr Watjana Lilaonitkul, Dr Susanne Gaube, Dr Julia Bailey  

Application deadline: Monday, January 26, 2026 

About the Project

You will join a hands-on supervisory team at the Global Business School for Health (GBSH), working at the intersection of computer science, human–computer interaction and clinical sexual health. The project sits within an active lab on safe and robust clinical AI and links closely to behavioural science, psychology and digital health communication. We meet regularly, agree focused research plans and support targeted outputs and career development from day one, including journal papers, conference presentations and portfolio pieces for academic and industry careers.

BACKGROUND:

Sexual difficulties are common. The British National Survey of Sexual Attitudes and Lifestyles reports that around half of women and two-fifths of men experience distress linked to erection difficulties, low libido, pain or orgasm difficulties. Many people hesitate to seek professional advice because of stigma, privacy concerns and uncertainty about whether help is possible or effective. Evidence shows that tailored digital sex therapy can be effective, yet there is still an unmet need for secure, trustworthy self-help advice that complements NHS services. At the same time, people are increasingly turning to general-purpose AI systems for sexual health advice, even though these tools were not designed for this setting and can generate unsafe, misleading or insensitive responses.

This PhD will explore how to design and deploy a privacy-preserving self-help AI system that provides evidence-based guidance and signposting for adults with sexual difficulties. The focus is on safe and responsible use of natural language processing (NLP) to support people to understand their concerns, try evidence-based strategies and, where appropriate, seek care from their GP or other medical professionals. The project will be co-created with stakeholders, people with lived experience and potential end users so that its design, safeguards and governance reflect real needs and values. Technically, you will develop an NLP stack tailored to sensitive health contexts, for example using retrieval-augmented generation, safety layers to reduce harm and misinformation risks, red-flag detection with rule-backed signposting and transparent explanations to support user trust. Robustness will be assessed through targeted test suites and adversarial evaluations of safety, calibration and refusal behaviour, leading to publishable evidence and a prototype for a theoretically informed, ethical and clinically safe generative AI tool.

AIMS:

The overarching aim is to design, build and evaluate responsible AI approaches that support evidence-based, user-centred self-help for sexual difficulties, with scope for the student to shape the technical emphasis. The project will move from co-design and requirements gathering with people with lived experience and clinicians, through technical development and validation of NLP and interaction approaches, to user evaluation and an implementation roadmap aligned with NHS pathways and governance requirements.

METHODOLOGY:

A mixed-methods approach combining literature review, co-design activities and interviews with people with lived experience, clinicians and other stakeholders; development and evaluation of an NLP pipeline in Python using appropriate large language models, retrieval components and safety layers; and qualitative and quantitative user evaluation, including usability testing and usage analytics.

TIMELINE:

• Year 1: Foundations and framing, including core training, scoping reviews, early stakeholder interviews, initial requirements and safety criteria, governance and data protection planning, ethics submissions and a first prototype.
• Year 2: Technical development and user studies, including iterative development of models, retrieval and safety components, user and stakeholder studies on usability, adoption and governance, and interim dissemination.
• Year 3: Consolidation and translation, including final optimisation and robustness checks, further usability work and, where feasible, external evaluations, manuscripts and conference submissions, thesis writing and an implementation roadmap.

ABOUT YOU:

We are keen to hear from candidates with a strong background in computer science, engineering, data science, human–computer interaction, applied mathematics or related quantitative disciplines, including clinicians or health researchers who can code confidently.

Essential

• A master’s degree in a relevant field, or equivalent experience, with strong Python programming and applied machine learning skills.
• Practical experience building end-to-end machine learning or NLP workflows, including data preparation, training and evaluation.
• Ability to write clear, modular and maintainable code, and willingness to adopt good software engineering practice such as version control, tests and documentation.
• Good experiment hygiene, including clear baselines, checks for overfitting, appropriate validation procedures and attention to reproducibility.
• Familiarity with GitHub or GitLab and collaborative development practices.
• Clear academic writing and ability to plan workstreams and meet deadlines.
• Motivation to work across disciplines and with stakeholders, including people with lived experience, clinicians and behavioural scientists.

Desirable

• Experience with health or behavioural science contexts, including qualitative methods and user studies.
• Familiarity with NLP tooling and evaluation, especially for sensitive or safety-critical topics.
• Experience with testing frameworks, experiment tracking tools or continuous integration in an ML context.
• Evidence of dissemination, such as preprints, posters, talks or open-source contributions.

WHAT WE OFFER: 

This studentship provides a starting stipend of £23,466 per annum and covers the cost of tuition fees based on the Overseas rate.  

ELIGIBILITY:

Home and Overseas students are welcome to apply.

DIVERSITY, EQUITY AND INCLUSION:

University College London is passionate about recruiting the best talent regardless of background. All assessments are made on merit. We have support systems to protect the physical and mental well-being of all our staff and students and will make every effort to accommodate your personal circumstances by adopting a flexible working and study pattern so that you can progress in your career while managing your circumstances.

HOW TO APPLY:

Enquiries regarding the post can be made to Sharleen Young ([email protected])

To apply, please send:

1. a current two-page CV
2. a one-sided A4 motivation letter
3. copy of transcripts and diploma and
4. the contact details of two professional referees

to Sharleen Young ([email protected]). Please use the following subject line: PhD application “TRUST: Transparent, Responsible, User-centred Sexual-health Technologies using Natural Language Processing”.

Closing deadline for applications: 23:59 Monday 26^th January 2026 (GMT summertime)

Interview date/s: TBC

Applications that are submitted without following the correct application process, or those exceeding the page limits for CV’s and motivation letters will not be considered. The successful applicant will subsequently be required to apply to and register on the Global Healthcare Leadership and Management MPhil/PhD to take up the studentship.