At a Glance
- Tasks: Develop noise-resilient AI models for real-world data challenges.
- Company: Collaborate with the National Physical Laboratory and top-tier academic institutions.
- Benefits: Generous stipend, funding for tuition fees, and research training.
- Other info: Join a dynamic research environment with excellent growth opportunities.
- Why this job: Make a real impact in AI by tackling complex data issues.
- Qualifications: Degree in Computer Science, Mathematics, Physics, or Engineering; AI experience required.
The predicted salary is between 18000 - 25000 € per year.
Real world datasets are often plagued by label and input noise due to variability in data collection, annotation errors, and incomplete records. Fully curating such datasets is costly and impractical, and models trained only on idealised or clean data often fail to generalise to other test sets in deployment. This project develops noise‑resilient AI models by jointly learning low‑dimensional representations for both the data and model parameters within the training phase to build models that learn from both aleatoric and epistemic uncertainty and become robust and generalisable. The project is in close collaboration with the National Physical Laboratory and benefits from the scientific environment and resources provided by the Centre for Vision, Speech and Signal Processing (CVSSP) and the Institute for People‑Centred AI at the University of Surrey.
About the Role
A generous stipend is offered in addition to funding for UK‑level tuition fees and research training.
Qualifications
- Degree in Computer Science, Mathematics, Physics, or Engineering.
- Prior experience in AI is necessary.
- Prior experience in tomographic imaging and medical physics would be advantageous but is not required.
- First Class undergraduate degree or MSc with Distinction (or equivalent overseas qualification) in mathematics, computer science, physics or engineering.
- Excellent mathematical, analytic, and programming skills.
- Previous experience in tomographic imaging would be advantageous.
Additional Information
Link to application – Robust Low‑Dimensional Representations for Noisy Real‑World Data at University of Surrey on FindAPhD.com. For further information, please contact Spencer Thomas, Principal Scientist at NPL.
Robust Low-Dimensional Representations for Noisy Real-World Data employer: National Physical Laboratory (NPL)
At the University of Surrey, we pride ourselves on fostering a collaborative and innovative work culture that empowers our researchers to tackle real-world challenges. With access to cutting-edge resources and a supportive environment, employees benefit from generous stipends, funding for tuition fees, and ample opportunities for professional growth in the field of AI. Join us in our mission to develop robust models that make a meaningful impact in the world of data science.
Contact Detail:
National Physical Laboratory (NPL) Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Robust Low-Dimensional Representations for Noisy Real-World Data
✨Tip Number 1
Network like a pro! Reach out to people in your field, especially those connected to the project. A friendly chat can open doors and give you insights that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio or any projects related to AI, make sure to highlight them. We love seeing practical applications of your knowledge!
✨Tip Number 3
Prepare for the interview by understanding the project inside out. Brush up on noise-resilient models and be ready to discuss how your background fits into the role. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we’re always looking for passionate candidates like you!
We think you need these skills to ace Robust Low-Dimensional Representations for Noisy Real-World Data
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your relevant skills and experiences that align with the role. We want to see how your background in Computer Science, Mathematics, or Engineering makes you a great fit for tackling noisy real-world data.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about this project and how your previous experience in AI can contribute to developing noise-resilient models. We love seeing enthusiasm!
Showcase Your Skills:Don’t forget to mention your programming skills and any experience with tomographic imaging if you have it. We’re looking for candidates who can demonstrate their analytical prowess and problem-solving abilities.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother application process. It’s the best way to ensure your application gets into the right hands quickly!
How to prepare for a job interview at National Physical Laboratory (NPL)
✨Know Your Stuff
Make sure you brush up on your knowledge of AI, especially in relation to noise-resilient models. Familiarise yourself with concepts like aleatoric and epistemic uncertainty, as well as low-dimensional representations. This will show that you're not just interested in the role but also understand the challenges involved.
✨Showcase Relevant Experience
If you've worked on projects involving tomographic imaging or similar fields, be ready to discuss them. Even if you don't have direct experience, think about how your skills in mathematics, programming, or previous AI projects can relate to the role. We want to see how you can apply your background to this specific challenge.
✨Ask Smart Questions
Prepare some insightful questions about the project and its collaboration with the National Physical Laboratory. This shows your genuine interest and helps you understand how you can contribute effectively. Think about asking how they handle data noise or what tools they use for model training.
✨Practice Problem-Solving
Expect to tackle some technical problems during the interview. Practise explaining your thought process clearly and logically. We recommend going through common AI-related problems and thinking about how you would approach them, especially in the context of noisy datasets.
