Material Scientist Summer Placement 2025

UK Atomic Energy Authority
South Oxfordshire District
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist - AI/ML (CADD) December 12, 2025

Higher/Senior Scientist in Quantum Computing and Machine Learning

Data Scientist

Principal Data Scientist London, United Kingdom

Principal Data Scientist

Data Scientist - Level 3

Company Description

By 2050, the planet could be using twice as much electricity compared to today. Are you interested in contributing and helping to shape the future of the world’s energy? If so, read on.

Fusion, the process that powers the Sun and Stars, is one of the most promising options for generating the cleaner, carbon-free energy that our world badly needs.

UKAEA leads the way in realizing fusion energy, partnering with industry and research for groundbreaking advancements. Our goal is to bring fusion electricity to the grid, supported by tomorrow's power stations. In pursuit of our mission, UKAEA embraces core values: Innovative, Committed, Trusted, and Collaborative.

Job Description

The Role

Are you looking for an exciting opportunity to make a difference? Join our team and contribute to the future of fusion energy.

We offer excellent opportunities for motivated and enthusiastic undergraduate students studying at UK Universities to join our 8-12-week summer placement scheme. The scheme is designed for students entering their penultimate or final year of studies, with potential opportunities post-graduation.

Our scheme gives you a unique opportunity to contribute to the development of one of the most advanced sources of sustainable and clean energy. During your summer programme, you will experience a broad range of diverse tasks, work on real projects, and gain invaluable experience within the fusion energy sector. UKAEA offers a nurturing and supportive community for you to gain valuable work experience in a fascinating and rapidly evolving industry.

Overview

Project title: Automating mechanical test result analysis of NEURONE steels

The objective of this project is to utilize Python or MATLAB to process raw data obtained from various mechanical tests, including tensile, creep, fracture toughness, and fatigue tests. The goal is to generate valuable engineering results, such as yield strength, ultimate tensile strength (UTS), and elongation values derived from tensile testing.

The student will integrate these results into a comprehensive material property handbook, employing a combination of Python and LaTeX for the final document creation. Additionally, the student will evaluate and rank each material based on their mechanical properties to identify the top-performing alloy variant.

Qualifications

Essential Requirements:

  • To be considered, you will need to be working towards a relevant degree and will be required to have the right to work in the UK.

Additional Information

A full list of our benefits can be foundhere.

UKAEA's mission is clean energy for all, and we welcome talented people from all backgrounds who want to help us achieve our mission. We are under-represented from some groups and so want to encourage applications in particular from women in STEM, people from Black British Caribbean and African backgrounds, and from Pakistani and Bangladeshi British backgrounds. Our Executive team, supported by our 'Head of Equality, Diversity and Inclusion' (EDI) and Wellbeing and our EDI Networks actively promote Inclusion and take steps to increase diversity within our organization. We reinforce best practices in recruitment and selection and evaluate approaches to remove barriers to success.

UK Atomic Energy Authority is committed to being accessible. Please email if you have any questions or require help or adjustments to compete on a fair basis, for example, changes to the way we interview or share information.

Please note that vacancies are generally advertised for 4 weeks but may close earlier if we receive a large number of applications.

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.