Outsmarting Evolving Pathogens with AI

About the Project

Zoonotic diseases, which jump from animals to humans, are one of the greatest public health challenges of our time. Pathogens are not static targets; they constantly evolve, creating new strains that can be more transmissible or evade our immune systems. Traditional mathematical models that predict how diseases spread often have a critical blind spot: they fail to account for this rapid evolution, treating the pathogen as a fixed entity.

This PhD project aims to change that. We are seeking a talented student to pioneer a new generation of disease models that can anticipate and predict pathogen evolution. The core of this groundbreaking work is to create a novel framework that integrates advanced Artificial Intelligence and evolutionary computation with established epidemiological models. Your mission will be to develop algorithms that simulate the evolutionary "optimization problem" pathogens face, predicting how they might change to become more successful.

You will apply and validate this innovative framework using two key zoonotic diseases with vastly different characteristics: the pandemic-potential avian influenza and the chronic, endemic Q fever. You will work with a uniquely interdisciplinary team of experts in mathematical modelling, microbiology, and nature-inspired computing.

The outcome of your research will be a powerful new tool to help policymakers and scientists make better decisions and prepare for future epidemics. You will develop a rare and highly sought-after skillset at the intersection of AI, data science, epidemiology, and evolutionary biology.

Academic qualifications

A 1st degree (minimum 2:1 classification) in one of the following subject areas:

• Computer Science or Artificial Intelligence
• Mathematics or Statistics
• Physics or Engineering
• Biological Sciences (e.g., Bioinformatics, Microbiology) with demonstrable, strong quantitative and programming skills.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Mathematical Modelling: An understanding of how to represent complex systems with equations.
• Programming: Experience in a language such as Python, R, or MATLAB is essential.
• AI / Machine Learning: Familiarity with core concepts of AI, optimization, or machine learning.
• Statistics: A solid foundation in statistical principles.
• Excellent analytical, quantitative, and problem-solving skills.
• A strong interest in interdisciplinary research that bridges computing, maths, and biology.
• The ability to think creatively and work independently.
• A passion for tackling major global health challenges

Desirable attributes:

• Experience with evolutionary algorithms or other bio-inspired computation methods.
• Knowledge of epidemiological models (e.g., compartmental or agent-based models).
• Experience in bioinformatics or computational biology.
• A background in microbiology or evolutionary biology

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact e.tingas@napier.ac.uk

Application Enquiries: https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?mP9MDnTs1Rwm8ftb3WVhDhXtraMQwXSUMdHC9wIc34es5bJqXf