Lancaster University

Application deadline: Monday 13 April

Find out about our internships, who can apply, and the stipend

Prospective projects

Below is a list of prospective projects you can apply for, complete with a short explanation and the lead supervisor's name and department. Please contact them before making your application; you will be asked if you have done this on the application form.

If you are interested to learn more about a specific project before applying, download the full project proposal at the bottom of the page.

GEM: a domain-specific modelling language for disease outbreak models. Chris Jewell, School of Mathematical Sciences

The GEM project is aiming to construct a domain-specific modelling language (DSML) for analysing infectious disease models for outbreaks such as Covid19, influenza, and malaria. Currently, the project lies in two halves -- a high-level model description language (https://gitlab.com/gem-epidemics/gem), and a low-level Bayesian inference library (https://gitlab.com/gem-epidemics/gemlib) written using Python/JAX. This project is about bringing these two halves together, developing a graph-orientated approach to transforming model descriptions to executable GPU code.

OutbreakLab: Building an Outbreak Simulator for Pandemic Preparedness. Jess Bridgen, School of Mathematical Sciences

Contribute to OutbreakLab, an outbreak simulation framework for pandemic preparedness and response modelling. This internship focuses on building an intuitive interface with dashboards, spatial maps, and dynamic visualisations that turn complex epidemiological simulations into insights for decision-making exercises. You will design tools to explore spatial spread, monitor key metrics, trigger interventions and view animated outbreak dynamics. As simulations are computationally intensive, High-Performance Computing enables parallel runs, high-resolution modelling, and scalable performance.

Automated pest detection in plant trials. Samantha Oates & Mathew Smith, Physics

We have partnered with RHS Wisley to determine if ‘companion planting”, whereby a second plant is grown alongside the first to attract predators that eat pests, can be applied at scale to minimise the global use of pesticides. In this internship, you will develop machine learning algorithms with a training set built from labels determined by the general public to automate the detection of invasive pests. You will update RHS Wisley Bug Watch with photographs of a new companion plant trial run by the RHS Wisley team, and advance initial efforts to develop AI algorithms to automate pest detection.

Revisiting the Risk Elicitation Puzzle: A Machine Learning Approach. Konstantinos Georgalos, Economics

This project revisits the “risk elicitation puzzle” in economics-the finding that individuals’ measured risk preferences vary across experimental methods. The internship will investigate whether a common latent structure of risk preferences can be recovered directly from choice data using machine learning techniques. The student will implement a computational pipeline combining dimensionality reduction and sparse autoencoders, using high-performance computing for large-scale model training and validation. The project will produce an open-source software pipeline for analysing risk data.

Detecting Gender Effects in Risky Choice: A Machine Learning Approach. Konstantinos Georgalos, Economics

This project investigates whether gender differences in risk preferences can be detected directly from experimental choice data using machine learning. Rather than relying on traditional econometric models, the project reframes the problem as a classification task: can gender be predicted from individuals’ lottery choices? The intern will develop a computational pipeline to train and evaluate machine learning classifiers on large experimental datasets using high-performance computing. The project will contribute to an open-source research tool for detecting gender effects in risky decision making

Preparation to complete the application form

These are the questions you will be asked on the application form. Please prepare answers ahead of time and cut and paste from your preferred text document.

This is the information you are asked for:

Full Name
Email address
Requested Project
Project supervisor
Why are you interested in Research Software Engineering? (max. 2000 characters)
Why have you chosen this project for this internship? (max. 2000 characters)
How will this internship assist you in your future career? (max. 2000 characters)

This is all that is required to complete you application. Please do not send test submissions through the form.

Submit your application

If you are having problems with your application, please contact us at enquiries@n8cir.org.uk