A Physics-Informed Machine Learning Approach to Epidemic Digital Twins

About the Project

Project Summary

Join us in developing next-generation AI tools for epidemic intelligence. This PhD will create Physics-Informed Digital Twins that combine epidemiology, machine learning, and optimisation to infer hidden infection dynamics and simulate public-health interventions. The project aims to deliver interpretable, uncertainty-aware models to support real-world epidemic preparedness and decision making.

Project Details

Recent pandemics such as COVID-19 exposed major weaknesses in current epidemic monitoring and forecasting models. Public health decision-making is often hindered by under-reporting of infections, delays and noise in epidemiological data, and rapidly changing transmission dynamics driven by behavioural, biological and policy factors. Traditional epidemiological models struggle to adapt to these changes, while purely data-driven machine learning approaches often lack the biological realism required for reliable policy support.

This 3-years, fully funded PhD project aims to address these challenges by developing a Physics-Informed Digital Twin (PIDT) for epidemic intelligence. The project will combine mechanistic epidemiological models with modern machine learning techniques to create a continuously updating computational model capable of monitoring epidemic dynamics, estimating hidden infection levels, and simulating intervention strategies.

The core idea is to integrate epidemiological compartmental models (such as SIR or SEIR models) with Physics-Informed Neural Networks (PINNs). PINNs are a class of machine learning models that embed physical or biological governing equations directly into the learning process. By incorporating epidemiological knowledge from biology and medicine into the training of neural networks, the resulting models maintain interpretability and realism while remaining flexible enough to adapt to noisy and incomplete real-world data.

Within this framework, the PhD researcher will develop methods capable of inferring time-dependent transmission parameters, hidden infection dynamics, and levels of under-reporting from incomplete epidemiological data. Bayesian uncertainty quantification techniques will be incorporated to ensure that the resulting predictions and simulations provide robust estimates together with confidence measures.

A key component of the project is the development of a Digital Twin framework for epidemic systems. Digital Twins are dynamic virtual models that continuously update as new data become available. In this project, the Digital Twin will ingest streaming epidemic data, update model parameters in real time, and simulate alternative scenarios of disease spread. These simulations will enable the evaluation of potential public-health interventions such as vaccination campaigns, social distancing measures, or healthcare capacity constraints.

To support policy analysis, the project will also integrate optimisation methods that allow intervention strategies to be explored within realistic social and economic constraints. Multi-objective and stochastic optimisation approaches will be used to study trade-offs between competing objectives such as reducing infection levels, limiting hospital stress, and minimising economic disruption.

The project sits at the intersection of applied mathematics, machine learning, epidemiology, and optimisation, and will contribute to the development of interpretable AI tools for epidemic preparedness and response. Expected outcomes include new methods for physics-informed machine learning in epidemiology, improved techniques for handling under-reported epidemic data, and an open-source software framework for epidemic Digital Twins.

The research will contribute to emerging efforts to develop AI-driven decision-support systems for public health, with potential impact for organisations such as public health agencies, healthcare systems, and government planning bodies.

Person Specification

Candidates should have been awarded, or expect to achieve, EITHER:

a] a First or Upper Second Class award in their undergraduate degree, in a relevant subject.

OR

b] a First or Upper Second Class award in their undergraduate degree, and a Merit or Distinction in a Masters degree, both in a relevant subject.

Qualifications from overseas institutions will be considered, but performance must be equivalent to that described above, and the University reserves the right to ascertain this equivalence according to its own criteria.

Desirable / Essential Skills or Experience

Essential: Programming skills (C/C++, Java, Python), knowledge of at least one of the following: data modelling, distributed systems or cloud computing.

Desirable: Some experience with data analytics, machine learning, or visualisation. Experience with software architecture. Optimisation, simulation algorithms. Familiarity with tools/platforms such as Docker, Git, cloud services, sensors, dashboards, or modelling tools. Publications, technical reports, or evidence of research potential.

Submitting an application

We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected.Your application must include:

1. English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
2. A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
3. A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
4. A Curriculum Vitae (Resume) which details your education and work history.
5. Two academic refereeswho can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
6. Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
7. A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

Location

This position will be based on the Aston Campus in Birmingham, UK. The successful candidate will need to be located within a reasonable distance of the campus, and will be expected to visit in person regularly.

Interviews

Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Key words

Machine Learning, Physics-Informed Neural Networks, Digital Twins, Epidemic Model

Funding Notes

This project covers all tuition fees and includes an annual stipend.

Please note that the successful candidate will be responsible for any costs relating to moving to Birmingham and/or visiting the Aston campus.