融合分布与无分布不确定性量化以实现可靠医疗决策博士研究员

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Postgraduate research project

Unifying distributional and distribution-free uncertainty quantification for trustworthy medical decisions

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

31 Jan 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

How much should a clinician trust a machine learning prediction, and where does the doubt come from? This project will unite distributional and distribution-free uncertainty quantification into one theory, and turn it into reliable tools for high-stakes clinical decisions, such as diagnosing glaucoma and modelling neurodegeneration.

Uncertainty in a prediction has several sources: limited training data, differences between hospitals and patient groups, and the inherent unpredictability of each outcome. Knowing which source dominates tells a clinician what to do next.

Two traditions study this:

• distributional approaches, such as hierarchical models, capture the full spread of plausible predictions and show where uncertainty comes from

• distribution-free approaches, such as conformal prediction, give guarantees without assuming a model

The two are complementary, yet have largely developed apart. You'll bring them together into one unified body of work.

You will:

• master the theory of uncertainty in machine learning: where it comes from, how it is represented, and what can be formally guaranteed about it

• develop methods that combine hierarchical uncertainty models with conformal guarantees

• establish what can be guaranteed for patients from hospitals seen during training, and from entirely new one

• design decision rules that know when to defer to a clinician, with provable error control

• apply them to problems such as glaucoma screening from retinal images and multiple sclerosis progression from brain magnetic resonance imaging (MRI)

This is foremost a fundamental research project: most of your time will go into theory and new methods for uncertainty quantification, while applications keep the work grounded. Clinicians at University Hospital Southampton, our collaborators, will help you validate and refine the methods.

The project is a joint venture between the Vision, Learning and Control group and the Digital Health and Biomedical Engineering group in the School of Electronics and Computer Science. You'll have access to the University's Iridis supercomputer and a lively machine learning and digital health community.

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

Dr Nery Riquelme Granada

New Frontiers Fellow

Supervisors

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

MN

Professor Mahesan Niranjan

ISIS Chair

Entry requirements

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

• mathematics

• statistics

• computer science

• physics

• a closely related quantitative subject

Essential skills:

• university-level probability and statistics: random variables, conditional probability and expectation, estimation, hypothesis testing

• programming in Python, used in at least one substantial piece of work such as a final-year or MSc dissertation, including NumPy and either PyTorch or scikit-learn

• comfort reading and writing mathematical arguments, such as the proofs in an undergraduate statistics or machine learning module

• The University's English language requirements for postgraduate research

Desirable skills:

• an MSc, or research experience, in machine learning or statistics

• familiarity with conformal prediction, Bayesian inference, or hierarchical (mixed-effects) models

• experience working with medical or imaging data

• a dissertation, preprint or paper

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 Dr Nery Riquelme Granada (N.A.Riquelme-Granada@soton.ac.uk).