面向个性化与预防性医疗的人工智能博士研究员

View all current projects

Postgraduate research project

Artificial Medical Intelligence for personalised and preventive healthcare

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 Jul 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

This project will investigate how medical imaging, multimodal and longitudinal health data can be used to model individual health trajectories, identify disease risk earlier, predict future outcomes, and support personalised prevention through trustworthy, causal and data-driven artificial intelligence.

Healthcare is increasingly moving from reactive treatment towards earlier prediction, prevention and personalised intervention. Artificial intelligence has an important role to play in this transition by learning from complex medical data to better understand individual health trajectories, disease risk and future outcomes.

This project will develop advanced methods in Artificial Medical Intelligence for personalised and preventive healthcare. The research will focus on intelligent models that can integrate and learn from rich health information, including medical imaging, multimodal measurements and longitudinal data.

Key research themes include:

• personalised risk modelling

• prediction of disease progression

• multimodal representation learning

• longitudinal machine learning

• causal and counterfactual reasoning

• generative modelling and digital twins

Particular attention will be given to developing AI systems that move beyond population-level prediction towards models capable of representing individual patients, their evolving health states and potential responses to interventions or preventive strategies. An important part of the research will also be the development of reliable and trustworthy AI, including uncertainty estimation, robustness, interpretability and rigorous evaluation of personalised predictions and counterfactual outcomes.

You'll join the Advanced Technologies for Translational AI Research (ATTAR) Lab, an active research environment focused on translational artificial intelligence for healthcare, and you'll work alongside PhD students, postdoctoral researchers and academic and clinical collaborators. You'll receive advanced research training in artificial intelligence and machine learning for healthcare, including:

• deep learning

• computer vision

• multimodal learning

• longitudinal modelling

• causal and counterfactual inference

• generative modelling

• trustworthy AI

Training will also include scientific communication, reproducible research, high-performance computing and publication in leading international venues.

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 Rahman Attar

SMIEEE, MIET, FHEA, PhD, MPhil, BEng

Lecturer

Entry requirements

You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:

• computer science

• artificial intelligence

• engineering

• mathematics

• a related quantitative discipline

Strong programming, mathematical and analytical skills are essential. Experience in machine learning, deep learning, computer vision or data science is highly desirable.

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 Rahman Attar (r.attar@southampton.ac.uk).