Newcastle University Summer Research Internship

Supervisor: Dr. Lee Higham

Dr. Higham’s Lab can offer projects in the area of asymmetric catalysis and fluorescent mitochondria imaging agents.

Supervisor: Dr. Chris O’Malley

Robotic assisted total knee arthroplasty (rTKA) is associated with increased accuracy of implant positioning and improved patient reported outcomes. rTKA enables the surgeon to position the implant accurately, and to adjust positioning to facilitate joint gap balancing with less soft tissue damage compared to manual TKA. This project will look to assess the data gathered from patients following rTKA surgery and correlate it to the pre-operative assessment of the patients, to model and predict whether there are early indications of post-operative success at the pre-operative stage of assessment. The project will be heavily MATLAB based and requires thorough data analysis and regression skills.

Supervisors: Dr. Heather Sugden, Professor Pip Moore, Dr. Marco Fusi

The Newcastle Marine Group we can offer places for up to 4 interns based at the University’s Dove Marine Laboratory out by the North Sea, taking projects in:

• Kelp forest ecology including taxonomy of organisms and kelp restoration and intertidal fieldwork looking at disturbance-recovery.
• Critical oxygen partial pressure (PO2) of marine organisms from intertidal habitats investigating the PO2 critical of these species under different treatments (e.g. thermal stress).
• Distribution and presence of invasive marine species and connectivity of habitats.

Supervisor: Dr. Nick Keepfer

This project explores the application of Neural Ordinary Differential Equations (Neural ODEs) and Physics-Informed Neural Networks (PINNs) in epidemiological modelling, specifically as a use-case to forecast infectious disease dynamics. It emphasizes the development of data-driven models using public datasets, like the one from Johns Hopkins University for COVID-19, to understand disease spread patterns. This initiative seeks to evaluate the utility of these computational techniques in modelling efforts, serving as a foundational exploration into their potential for enhancing our understanding of infectious disease behaviour and spread.

Supervisors: Dr Nicola Hewett and Dr Nick Keepfer

This project explores the application of Neural Ordinary Differential Equations (Neural ODEs) and Physics-Informed Neural Networks (PINNs) in epidemiological modelling, specifically as a use-case to forecast infectious disease dynamics. It emphasizes the development of data-driven models using public datasets, like the one from Johns Hopkins University for COVID-19, to understand disease spread patterns. This initiative seeks to evaluate the utility of these computational techniques in modelling efforts, serving as a foundational exploration into their potential for enhancing our understanding of infectious disease behaviour and spread.

Supervisor: Dr. David Rosario

The student will explore our understanding of the influence of supermassive black hole “feedback” on the gas content of distant galaxies. Starting with a targeted literature review, the student will then learn how to obtain, process, and measure the gas content of a sample of galaxies from the KMOS AGN Survey at Hi-z (KASHz) programme observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We will search for the evidence for feedback using these measurements and information from literature studies.

Supervisor: Dr. David Rosario

The student will learn to operate and characterise the two observing systems (including telescopes, mounts, cameras, accessories and control software) that belong to the School of Mathematics, Statistics and Physics (MSP). They will develop and document a set of observing experiments that will be used as practical learning components of astronomy modules in the Physics curriculum. Good computing skills and a basic familiarity with introductory astronomy are desirable.

Supervisor: Dr. Lee Fawcett

Road safety practitioners apply safety countermeasures (e.g. speed cameras) reactively, once a collision threshold has been exceeded at a particular accident hotspot. Using Bayesian methods, our aim is to predict collision counts at hotspots and apply treatments proactively, before the collision threshold is over-topped, allowing authorities to carefully target road safety budgets on countermeasures and prevent collisions before they happen. We will work with data provided by the Florida State Department of Transport to investigate Bayesian models for road safety hotspot prediction, with a particular interest being given to the effects of climate on road traffic collisions in the Florida region.

Supervisor: Dr. Lee Fawcett

In 1859, a solar coronal mass injection hit the Earth’s magnetosphere inducing one of the largest geomagnetic storms on record. Observed and recorded by Richard Carrington, the storm produced aurorae visible in the northern hemisphere as far south as Cuba, and telegraph systems failed across Europe and North America.

Since then, there have been several ‘near misses’, including the solar storms observed on Halloween in 2003. What would happen if a Carrington-like event occurred today? How often might we expect to see such an extreme solar storm? Today, much more is at stake. We might expect mass disruption to global positioning systems and satellite communications, and disruption to the electrical grid — especially in highly interconnected areas such as the US Eastern seaboard. Recent attempts at modelling solar flares suggest we can expect to see a Carrington-like event somewhere between once every 33 years and once every 950 years!

Little has been done to develop statistical models for solar flares. In this project, we will work within the Bayesian framework to develop appropriate extreme value models for solar flare extremes. In particular, we will look to exploit the complex cyclic structure present in solar flares datasets, as well as the temporal dependence observed. one aspect of the project will involve the formulation of expert prior distributions through collaborative work with astrophysicists.

Supervisor: Dr. Joseph Matthews

“Home field advantage” is one of the most commonly used ideas when trying to assess how well a competitive sports team is likely to do in an upcoming game, supposedly playing at your home stadium in front of your own fans will spur you on to perform better than if you were at your opponent’s stadium in front of their fans. The goal of this project is to use data and statistical modelling to ask: is this actually true? Do teams get a boost from playing at home, and if so, do all teams get the same boost or does it vary?

Later, we’ll then consider games which had home field advantage but without crowd support, e.g. when games had to be played in empty arenas due to the Covid-19 pandemic to ask: How much do fans specifically contribute? Can we identify which team’s fans give them the biggest boost to performance?

Supervisor: Dr. Shirley Coleman

Working with a data from a local dental practice, the project involves analysing data including appointment booking systems, attendance records and treatment data using descriptive, graphical and statistical modelling techniques. Supervised by Dr Shirley Coleman with support from the dental practitioners.

The project will require the student to be familiar with basic statistics such as correlation, summary statistics and graphical presentations. Some data visualisation skills using mapping software would be good and can be learnt during the project. Use may be made of open data such as that from the Office for National Statistics (ONS) and Eurostat. Summer projects are task focused and may include some elements of data cleaning and routine work. Data is provided in Excel spreadsheets and students will be expected to undertake appropriate data preparation and manipulation.

Business partners often like to see established analytical methods used such as linear regression but these can be augmented, for example by exploring lasso and ridge regression. Logistic regression and chi-square techniques are handy to know for dealing with nominal or ordinal target variables/ quantities of interest. Cluster analysis is often appropriate. Machine learning techniques such as decision trees and random forests are useful and can be explored using R and/or Python.

It may be convenient to make use of industry standard software available at the university such as MINITAB, SPSS, SAS or JMP to corroborate results.

Industry based summer projects require a certain level of confidentiality as internal data is involved. Each summer student writes a report, typically 10 to 30 pages long which should be suitably redacted for confidentiality. Once agreed by the business partner, the project report can be used by the student for their reference and to support their CV.