Summer Internship - Data-Driven Engineering

First Light Fusion Ltd is a pioneering UK-based fusion energy company at the forefront of developing breakthrough inertial fusion technology. At the heart of our mission is FLARE — First Light’s next-generation fusion approach designed to accelerate the path toward practical, clean, and limitless energy. By combining world-leading expertise in advanced physics, simulation & machine learning, and innovative engineering, FLARE represents a bold step forward in delivering scalable fusion solutions.

Alongside our fusion program, First Light Fusion also delivers commercial services and partnerships applying our simulation tools and hydrodynamic amplification technology to solve complex challenges across a range of advanced industries.

Joining First Light Fusion means being part of an ambitious team shaping one of the most exciting energy transformations of our time.

Job Description

Role Purpose

A key engineering challenge in the FLARE reactor concept is the efficient transport of energy from the capacitor banks to the fusion target. The capacitors are part of the permanent machine and therefore operate at relatively low power density, whereas the target is a single-shot component subjected to high power density. The power conduit connecting these components, known as a recyclable transmission line (RTL), must satisfy several competing constraints, most notably minimising system inductance while avoiding electrical breakdown under extreme electromagnetic fields. This summer internship will explore the design of RTLs, using FEA and data science approaches.

The project will make use of FLF’s advanced data science framework, with structured weekly mentoring. The work will support future careers in Inertial Fusion Energy, data-driven engineering and Pulsed Power Sciences.

Accountabilities and deliverables

• Explore parametric representations of RTLs
• Model RTLs using finite element analysis (FEA), and establish robust methodologies to minimise artificial field enhancement due to mesh resolution
• Perform optimisation studies based using heuristics or surrogate models
• Investigate low-inductance transmission line designs for regions external to the reactor
• Develop practical design rules for geometrically self-consistent machine configurations

Core skills, knowledge and attributes

Essential

• Studying towards a degree in a STEM subject
• A strong understanding of electromagnetics
• Experience of programming e.g. python, Matlab

Desirable

• Understanding of data science tools, such as optimisation and machine learning
• Experience using FEA software, ideally electromagnetics