Newcastle University

Explainable health data analytics with tsetlin machines

Intern: Jamie Jeays
Degree course: BEng Hons Elec & Electronic Eng
LinkedIn Profile
My interest in engineering, specifically electrical and electronic stems from being one of two female students in my A-level physics class, through which I surpassed the people around me through the electronics module. As someone who is passionate about gender equality I felt very motivated to stay in this field as I can advocate from a feminist standpoint while still working to benefitting the world.

Why did you apply for this internship?

I applied as some of the projects stood out to me as aligning with my career goals and will compliment my degree and help develop my experience and skillset.

What do you hope to gain in completing this project?

I hope to gain a better understanding of working on a professional project and understand machine learning and data analytics with a more developed skillset.

Data Pre-processing and Generation for Real-World Neural Architecture Search Applications

Intern: James Kaye
Degree course: Mathematics

I have a strong interest in applied mathematics, data analysis and machine learning. I chose to study mathematics because I enjoy problem-solving and the way mathematical ideas can be used to understand complex systems. Throughout my degree, I have particularly enjoyed modules involving statistics, coding and mathematical modelling, as they have shown me how quantitative methods can be applied in the real world.

This internship interests me because it gives me the opportunity to develop my research and programming skills while working on a project connected to real-world neural architecture search applications. I am especially looking forward to learning more about data pre-processing and how machine learning methods can be made more effective.

Outside my degree, I enjoy keeping active, playing and following football and I have a passion for music and playing instruments.

Why did you apply for this internship?

I applied because I wanted to gain experience working on a real research project that combines mathematics, coding and machine learning. The project particularly appealed to me because it focuses on data pre-processing and generation for real-world neural architecture search applications, which is an area I am keen to understand more deeply. I saw it as a valuable opportunity to develop my technical skills while learning how computational research is carried out in practice. I was also interested in the chance to work with experienced supervisors and contribute to a project with practical relevance beyond my degree.

What do you hope to gain in completing this project?

Through this project, I hope to develop a stronger understanding of how computational research is carried out in practice, particularly in relation to data pre-processing and data generation. I would like to improve my programming and analytical skills, while also gaining experience of working more independently on a research-focused project. I am keen to learn how neural architecture search can be applied to real-world problems and what challenges arise when preparing data for these applications.

Overall, I hope the internship will help me build confidence in research and strengthen my technical skillset for future study or careers involving data, technology or applied mathematics.

Calculating Long-Time Quantum Trajectories Using Arbitrary Precision Floating Point Numbers on COMET

Intern: George Maniadakis

Deployed End-to-End Machine Learning on an Autonomous Racing Car

Intern: Shamik Srimany
Degree course: Bsc. Computer Science
LinkedIn Profile

I am a second-year student and chose Computer Science because I have been passionate about programming since I was around 13, when I first taught myself C++. What draws me to the subject is the combination of rigorous problem-solving and real-world impact — I find it most satisfying when code does something tangible, whether that is a deployed application or a system that has to work under genuine constraints.
Outside my degree I have built a full-stack application from scratch for a school in collaboration with a non-technical stakeholder, taught Java to peers as part of a programming club, and currently work as a Peer Mentor at Newcastle supporting first-year students through their transition to university.

Why did you apply for this internship?

I applied because it offers something coursework cannot: the experience of deploying a machine learning model on real physical hardware, under real constraints, and testing whether it actually works. The F1Tenth project sits at the intersection of ML, robotics, and systems engineering — exactly the kind of applied challenge I find most compelling.

My technical background in Python, Linux, and Docker, combined with prior experience deploying software end-to-end, means I can contribute meaningfully from the start while using the internship to bridge the gap between my theoretical ML knowledge and real deployed systems. I am also considering postgraduate study, and this project gives me direct exposure to what computing research looks like in practice — something I cannot get from a degree alone.

What do you hope to gain in completing this project?

By the end of this internship I hope to have deployed a machine learning model on physical hardware — something I have never done before. Specifically, I want to develop practical skills in working with real sensor data pipelines, optimising models for embedded deployment, and evaluating performance in a dynamic physical environment rather than on a static benchmark dataset.

Beyond the technical skills, I hope to gain a clearer understanding of what research in computing actually looks like in practice — how to frame a problem, engage with existing literature, design experiments, and handle results that do not go as expected. I am considering postgraduate study and this internship will help me assess whether research is the right direction for me.

From Simulation to AI: Physics-Informed Neural Networks for Chemical Kinetics in Turbulent Hydrogen Swirling Flames

Intern: Adam Wilson
Degree course: MEng Mechanical Engineering
LinkedIn Profile

I am an MEng Mechanical Engineering student specialising in computational analysis. While my academic foundation is in engineering, my primary strength and interest lie in the computational infrastructure required to solve complex, data-heavy problems.

Why did you apply for this internship?

My third-year project was a turning point for me; it made me realise that the bottleneck in modern data-heavy research is often the software infrastructure. This experience shifted my career ambitions toward becoming a Research Software Engineer (RSE) with an emphasis on engineering simulation software.

The N8 CIR internship provides the optimal environment to master professional software engineering standards, bridging the gap between my proven analytical capabilities and enterprise-grade research infrastructure.

I am excited to work on this project with all my focus. Presenting my work to the panel and other interns will be a valuable opportunity, challenging my understanding of the project and presentation skills.

What do you hope to gain in completing this project?

I intend to master the technical stack required for modern, GPU-accelerated machine learning workflows. I aim to develop Physics-Informed Neural Networks (PINNs) using PyTorch.

Furthermore, I want to learn professional RSE practices: building modular data pipelines, utilising strict version control (Git) for collaboration, and writing scalable code for parallel architecture using the Bede HPC system. Mastering these competencies will ensure I successfully benchmark AI surrogate models for this project and equip me with the rigorous technical framework necessary to excel in a research software engineering career.