可信人工智能用于结合遗传与临床数据的个性化疾病风险预测博士研究员

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Postgraduate research project

Trustworthy AI for personalised disease risk prediction using genetic and clinical data

Funding

Competition funded

Competition funded

View fees and funding

Type of degree

Doctor of Philosophy

Entry requirements

2:1 honours degree

2:1 honours degree

View full entry requirements

Faculty graduate school

Faculty of Engineering and Physical Sciences

Closing date

17 Jun 2027

On this page

About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

About the project

Why do people with similar genetic risk develop different health outcomes, and can AI identify who is most at risk? This project will develop uncertainty-aware statistical and machine learning methods for personalised disease risk prediction, combining genetic information, clinical measurements and potentially longitudinal health data.

Genetic information and clinical measurements offer new opportunities for personalised disease risk prediction. However, individuals with similar genetic profiles may experience very different health outcomes. AI models often overlook this variability and the uncertainty arising from incomplete data, measurement errors and differences between populations. This can lead to unreliable or overconfident predictions.

This project will develop statistical and machine learning AI methods to improve personalised risk prediction by explicitly representing and quantifying uncertainty in genetic and clinical data. Your research will address three interconnected challenges:

• multimodal risk prediction: Integrating genetic, clinical and imaging-derived biomarkers to develop personalised risk models

• uncertainty quantification: Developing hierarchical models that distinguish different sources of uncertainty and assess the reliability of individual predictions

• reliable decision support: Using computer simulations and real-world data to evaluate prediction accuracy, bias, calibration and uncertainty, with particular attention to clinical decision-making

The primary application will focus on metabolic diseases, particularly type 2 diabetes and obesity-related complications. The project will investigate how genetic predisposition, clinical biomarkers and their changes over time contribute to disease risk, using suitable population-based and clinical datasets. You'll work at the intersection of statistical methodology, genetic epidemiology and trustworthy AI, developing methods that move beyond accurate population-level predictions towards reliable and clinically meaningful individual risk assessments.

You'll receive interdisciplinary training in statistical modelling, machine learning, genetic epidemiology and uncertainty quantification. Training will include analysing large-scale genetic and longitudinal clinical datasets, designing simulation studies, assessing prediction reliability and bias, and applying reproducible research practices. Supervisors will support scientific writing, conference presentations, collaboration and career development.

The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.

Potential supervisors

Lead supervisor

Professor Gabriela Czanner

Professor in Digital Health

Research interests

• Spatial statistical models for clinical images to help understand diseases

• Longitudinal and spatio-temporal models for early detection, screening and monitoring of diseases

• Uncertainty quantification for predictions from statistical and machine learning models

Supervisors

Professor Hanieh Yaghootkar

Professor of Health Data & Technology

Research interests

• Genetic epidemiology

• Precision medicine

• Fat distribution

Entry requirements

A UK 2:1 honours degree, or its international equivalent, in one of the following:

• statistics

• mathematics

• computer science

• data science

• bioinformatics

• engineering

• a related quantitative discipline

Fees and funding

We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.

Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

For more information, please visit our postgraduate research funding pages.

How to apply

Apply now

You need to:

• choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences

• select Full time or Part time

• search for programme PhD Computer Science (7089)

• add name of the supervisor in section 2 of the application

Applications should include:

• research proposal

• your CV (resumé)

• 2 academic references

• degree transcripts and certificates to date

• English language qualification (if applicable)

Contact us

Faculty of Engineering and Physical Sciences

For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).

Project leader

For an initial conversation, email Professor Gabriela Czanner (G.Czanner@soton.ac.uk).