多发性硬化症进展可靠预测博士研究员（使用共形预测）

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Postgraduate research project

Reliable forecasts of multiple sclerosis progression using conformal prediction

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

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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

Multiple sclerosis progresses differently in every patient, and knowing who is likely to decline fastest could change their care. This project will develop machine learning methods that forecast disability progression with reliable, guaranteed uncertainty, built on conformal prediction and shaped with neurologists at University Hospital Southampton.

Multiple sclerosis (MS) affects more than 150,000 people in the UK, and no two patients follow the same course. Machine learning can forecast disability from clinical scores, MRI and wearable activity data, but a forecast only helps if a clinician knows how far to trust it for that particular patient.

Conformal prediction attaches a range to each forecast that is guaranteed to contain the true outcome at a chosen rate, without assuming the model is correct. Our recent work on a Southampton MS cohort showed that this guarantee can hold on average while hiding which patients are missed.

You will:

• develop conformal methods whose guarantees hold for clinically meaningful groups of patients, not only on average

• adapt them to the realities of clinical data: small cohorts, several data types, and repeated visits over time

• test whether findings replicate across hospitals and in large clinical trial datasets

• work with neurologists to turn uncertainty estimates into information that supports monitoring and treatment decisions

The project combines new methodology with clinical impact, and is part of a wider programme on reliable machine learning for MS. You'll join a growing team of students working on uncertainty in MS, collaborate closely with the neurology team at University Hospital Southampton, and have access to the University's Iridis supercomputer.

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

Entry requirements

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

• computer science

• mathematics

• statistics

• data science

• biomedical engineering

• a closely related quantitative subject

Essential skills:

• university-level probability and statistics: random variables, conditional probability, estimation, regression

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

• interest in healthcare, and willingness to work closely with cliniciansand to learn the clinical background of MS

• the University's English language requirements for postgraduate research

Desirable skills:

• an MSc, or research experience, in machine learning, statistics or health data science

• familiarity with conformal prediction or other uncertainty quantification methods

• experience with clinical, longitudinal or medical imaging (MRI) 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).