Undergraduate Placement - Materials & Systems Modeller
🚀 Off-cycle Internship
AI generated summary
- The candidate must possess a growth mindset, be curious and innovative, have resilience and be a team worker.
- The candidate will work on characterizing material ageing processes, analyzing data using statistical techniques, writing and running codes in various languages, utilizing computational techniques for optimization, and verifying and validating codes using suitable test cases.
- As an undergraduate student within the Modelling Team in Materials and Analytical Science, you will get the opportunity to work in an exciting part of AWE through projects which may include working on a range of models and codes, utilising several coding languages, helping deliver our mission.
- We are interested in applications from candidates working towards a degree in Mathematics, Physics or Computer Science as well as Materials Science or Chemistry students who can demonstrate strong maths or programming skills. Most importantly we are looking for enthusiastic individuals keen to get involved and work on interesting research and tasks. The Modelling Team work on the full model development lifecycle.
- You will possess the following traits and qualities:
- Growth Mindset; You’re full of grit and determination. Clear where you are going and how you will get there.
- Curious; you’re a person who loves to think outside the box. (You’re a person who loves to take the box apart!).
- Innovative; you’re open to learning about new technologies and ground-breaking ideas.
- Resilience; you’re an advocate of ‘speaking up’ for yourself and others openly and constructively.
- Team Worker; you’re dependable and reliable. You’re interested in collaborating with and supporting others.
Area of Responsibilities
- Some of the projects you may get to work on include:
- Working with other Materials and Analytical Scientists to characterise material ageing processes, proposing approaches to model the observed trends
- Using a variety of statistical techniques to analyse experimental data
- Writing, developing and running codes written in a range of languages (e.g. MATLAB, Mathematica, OpenFOAM and R)
- Utilising computational techniques for parameter optimization
- Verifying and validating codes by identifying suitable test cases, determining analytical solutions where appropriate
- A market leading contributory pension scheme
- Generous holiday entitlement
- Excellent work/life balance
- 9-day working fortnight. This means you get every other Friday off work.