How do you make things with less waste, less energy and materials that last longer?

This cluster works on eco-friendly materials, low-carbon production and circular materials that can be reused or recycled, and its questions are often the ones that stall real factories. One ongoing study examines the thermal behaviour of low-temperature tin-bismuth solder for energy-efficient electronics packaging, the kind of question that decides whether a packaging line can take on the cooler, more efficient chips that AI hardware increasingly demands. Another develops heat source models and grain size prediction for wire arc additive manufacturing (WAAM), helping engineers predict how thick metal structures will behave before they are printed. Picture yourself running simulations in the morning, comparing them against physical samples in the afternoon, and spending the evening working out why the two disagree.

How do you keep clean power flowing when the sun and wind will not cooperate?

This cluster covers smart grids, energy storage and optimisation, fluid dynamics and the low-carbon infrastructure behind the clean energy transition. It is also home to some of the school's more surprising ideas: Taylor's researchers have developed a hybrid solar cell designed to generate power in rain as well as sunshine, a direct response to the reality of tropical weather. A candidate here might spend a semester testing how a storage system or control algorithm handles sudden changes in load, the kind of fluctuation a grid faces every afternoon when clouds roll in.

What happens to everything industry leaves behind?

This cluster focuses on advanced water and wastewater treatment, air pollution control, biomass processing, and turning waste into valuable resources. Much of the work involves industry partners who bring real problems to the table. One project investigates a solar-powered evaporator to treat industrial wastewater from an electronics manufacturer; another tests temperature-responsive membranes that resist clogging in kitchen wastewater. Research here tends to end with a question every candidate learns to expect: will it work at scale, and what will it cost?

How do you build a machine that people can trust to act on its own?

This cluster works on drones, robots whose decisions are trustworthy and explainable, and smart manufacturing systems connected through the Internet of Things (IoT). A candidate might spend weeks refining a drone's control algorithm in simulation, then test whether it holds steady once real wind, real obstacles and real sensor noise enter the picture.

What does it take for millions of connected devices to talk to each other reliably?

This cluster covers smart sensors, adaptive wireless communication, embedded IoT systems and edge computing, where data is processed close to where it is collected instead of in a distant data centre. One current project uses machine learning to understand how user behaviour affects the performance of 5G networks, with the aim of using network resources and energy more efficiently.

How can a factory predict a problem before it happens?

This cluster applies machine learning and computational intelligence to industrial systems, building predictive models and optimising processes. Think of a production line that flags a failing component days before it breaks, or a plant that adjusts its own settings to cut energy use without losing output.

What happens when the systems running a power grid or a factory come under attack?

This cluster protects cyber-physical systems, smart grids and the industrial control infrastructure that essential services depend on. As more engineering systems connect to networks, securing them becomes an engineering problem as much as an IT one.