Health applications of Dynamic Bayesian Networks

Are you passionate about AI’s potential to solve real-world challenges? Join the Healthcare Ecosystems theme of the AI for Collective Intelligence Hub as a PhD student and contribute to cutting-edge research aimed at using large-scale health databases to improve collaborative decision making between clinicians, patients and policy makers.

Overview of the Research:

Bayesian Networks (BNs) are a popular class of models which capture complicated, multivariate relationships through a graphical structure that is interpretable for both statistical and non-statistical users. While BNs are frequently used in health applications, they are often fit to data representing a single snapshot in time and therefore can only detect associations between risk factors and health outcomes. Together with large-scale longitudinal data, casual network models such as Dynamic Bayesian Networks (DBNs) can help us model multivariate temporal trends and identify the casual relationship present among risk factors and health conditions.

In this project, you will explore and develop novel DBN-based methods to find casual relationships with potential applications in a variety of health areas including

·        Explainable forecasts of hospital bed capacity

·        Personalised predictions from diabetes wearables

·        Early warning of disease outbreaks based on wastewater samples

You will work alongside a growing network of PhD students and postdocs in health data science at the University of Bath and benefit from a wide range of development and networking activities across the other nodes of the Hub at Bristol, Cardiff, Exeter, Glasgow, UCL and Ulster. This PhD is an excellent opportunity to build on your analytics skills in a high-impact project, preparing you for a future career in health research in academia or industry.

Project keywords: Bayesian networks, time series, health data science

Candidate Requirements:

Applicants should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent) in a relevant subject such as health data science, epidemiology, statistics, computer science or clinical training. A master’s level qualification and/or relevant clinical experience would also be advantageous.  Experience with data analysis (using a language such as R, Python or Stata), ideally through independent project work, is essential.

Enquiries and Applications:

Informal enquiries are encouraged and should be directed to Dr Theresa Smith [email protected]

Formal applications should be submitted via the University of Bath’s online application form for a PhD in Statistics prior to the closing date of this advert.

IMPORTANT:

When completing the application form:

1.      In the Funding your studies section, select ‘University of Bath LURS’ as the studentship for which you are applying.

2.      In the Your PhD project section, quote the project title of this project and Sandipan Roy as the lead supervisor. 

Failure to complete these two steps will cause delays in processing your application and may cause you to miss the deadline.

More information about applying for a PhD at Bath may be found on our website.

PLEASE BE AWARE: Applications for this project may close earlier than the advertised deadline if a suitable candidate is found. We therefore recommend that you contact the lead supervisor prior to applying and submit your formal application as early as possible.

Equality, Diversity and Inclusion:

We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.