Leveraging Electronic Health Records: Bayesian Joint Longitudinal and Survival Modelling of Bivariate Longitudinal Trajectories for Two Disease Outcomes

We are thrilled to announce an exceptional PhD opportunity in medical statistics with machine learning, specifically tailored for Electronic Health Records and Big Data analysis. This project is designed for candidates eager to explore and expand the realms of statistical analysis and reliable machine learning, with a special emphasis on joint longitudinal and time-to-event data modelling.

 Applications accepted all year-round Self-Funded PhD Students Only 

Key Research Areas

The successful candidate will embark on a journey of discovery and innovation in several key areas:

Bayesian Joint Longitudinal and Survival Modelling for EHR Data

• Investigate Existing Bayesian Frameworks:

Explore and adapt current Bayesian methods for joint longitudinal and survival modelling to accommodate the complexities of EHR-derived longitudinal data.

• Develop Innovative Bayesian Models:

Create Bayesian models that seamlessly integrate bivariate longitudinal trajectories derived from EHR data and survival outcomes, capturing their joint dependencies and complexities.

• Explore Efficient Computational Algorithms:

Develop efficient computational algorithms for Bayesian inference in joint models tailored to EHR data, ensuring scalability and computational feasibility.

• Evaluate Methodologies:

Conduct extensive simulation studies and real-world applications using EHR data to assess the performance and generalizability of the proposed models.

• Provide Practical Resources:

Offer comprehensive software implementations, practical guidelines, and educational resources to facilitate the adoption and utilization of Bayesian joint modelling techniques for analysing EHR-derived bivariate longitudinal trajectories and survival outcomes by healthcare researchers and practitioners.

What We Offer

• A platform to work on pioneering research with tangible industrial applications.
• Access to cutting-edge facilities and resources.
• A nurturing and collaborative research community.
• Expert guidance and mentorship from leaders in the field.
• Embark on a transformative journey with us, where your passion for statistics machine learning, and big data analysis can contribute to the future of Health Care research. We look forward to welcoming an innovative and dedicated researcher to our team.

Candidate Profile

We are seeking a candidate who:

• Has a profound interest in the intersection of statistics, machine learning, and big data analysis.
• Is eager to contribute to groundbreaking research in health care research.
• Possesses a strong foundation in mathematics, statistics, or a related field.
• Is experienced or keen to learn advanced machine learning techniques.
• Is motivated to collaborate in a dynamic and innovative research environment.

Funding Notes

• This project is offered for self-funded students only, or those with their own sponsorship or scholarship award.

Diversity statement

Our research community thrives on the diversity of students and staff which helps to make the University of Dundee a UK university of choice for postgraduate research. We welcome applications from all talented individuals and are committed to widening access to those who have the ability and potential to benefit from higher education.