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- Tasks: Develop advanced deep learning models for predicting semiconductor fabrication outcomes.
- Company: Join a pioneering team in semiconductor manufacturing and machine learning.
- Benefits: Gain hands-on experience, publish research, and collaborate with industry leaders.
- Why this job: Make a real impact in tech by optimising manufacturing processes with AI.
- Qualifications: Strong background in machine learning and data analysis required.
- Other info: Opportunity to supervise PhD students and shape research direction.
The predicted salary is between 40000 - 50000 £ per year.
Semiconductor fabrication is one of the most complex and precision-driven forms of manufacturing. At nanometre scales, even subtle variations in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning directly from experimental and process data, these models will enable a shift from iterative, trial-and-error fabrication towards predictive and data-driven manufacturing.
Responsibilities
- Develop advanced deep learning models capable of predicting post-fabrication device characteristics from process parameters.
- Engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements.
- Collaborate with fabrication engineers to translate physical processes into machine learning models; design, train, and evaluate deep learning architectures; assess generalisation across different process conditions.
- Validate models experimentally and test at industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation.
- Supervise PhD students and junior researchers; contribute to shaping the research direction of the team.
- Contribute to development of innovative technologies with a clear pathway to commercialisation through the spinout company Deep Fabrication.
Qualifications / Role details
The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact. The role will provide deep exposure to nanofabrication processes, experience with industry-relevant datasets and problems, and opportunities to publish in leading journals and conferences. It is particularly well-suited to candidates who are motivated by applying machine learning to real-world systems where the underlying physics is complex and not fully understood.
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Duration
This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months.
Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in London employer: Cyber Security Academy Southampton
Contact Detail:
Cyber Security Academy Southampton Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the semiconductor and machine learning fields on LinkedIn. Join relevant groups and engage in discussions to get your name out there and learn about potential job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to deep learning and semiconductor manufacturing. This can be a game-changer during interviews, as it gives you a chance to demonstrate your expertise and problem-solving abilities.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of machine learning models and their applications in fabrication processes. Practice explaining complex concepts in simple terms, as this will help you communicate effectively with both technical and non-technical interviewers.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate individuals who want to make an impact in the field. Keep an eye on our job listings and submit your application directly for the best chance at landing that dream role.
We think you need these skills to ace Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning and semiconductor manufacturing. We want to see how your skills align with the role, so don’t be shy about showcasing your projects and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about applying machine learning to nanoscale semiconductor manufacturing. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Research Experience: If you've worked on any projects involving deep learning or complex datasets, make sure to mention them. We’re looking for candidates who can engage with high-dimensional data, so share your relevant experiences!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re keen on joining the StudySmarter team!
How to prepare for a job interview at Cyber Security Academy Southampton
✨Know Your Stuff
Make sure you brush up on the latest advancements in machine learning, especially as they relate to semiconductor manufacturing. Familiarise yourself with deep learning models and how they can predict fabrication outcomes. Being able to discuss specific techniques or recent papers will show your passion and expertise.
✨Understand the Industry
Dive into the world of semiconductor fabrication. Understand the challenges faced in nanoscale manufacturing and how machine learning can address these issues. This knowledge will help you engage meaningfully with interviewers and demonstrate that you're not just a tech whiz but also someone who understands the practical applications of your work.
✨Prepare for Collaboration Questions
Since the role involves working closely with fabrication engineers and supervising PhD students, be ready to discuss your experience in collaborative environments. Think of examples where you've successfully worked in teams, resolved conflicts, or mentored others. This will highlight your ability to contribute positively to the team dynamic.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle hypothetical scenarios related to deep learning model validation or dataset challenges during the interview. Practise articulating your thought process clearly and logically. This will demonstrate your analytical skills and your ability to think on your feet, which is crucial for this role.