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- Tasks: Develop deep learning models to optimise semiconductor manufacturing processes.
- Company: Leading research team focused on innovative semiconductor technologies.
- Benefits: Gain hands-on experience, publish research, and collaborate with industry leaders.
- Why this job: Make a real impact in tech by applying machine learning to complex challenges.
- Qualifications: Experience in machine learning and a passion for solving real-world problems.
- Other info: Join a diverse team committed to inclusivity and groundbreaking research.
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 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
✨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 show your passion for the industry.
✨Tip Number 2
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with deep learning models and how they can be applied to real-world problems in semiconductor manufacturing. We want to see your expertise shine!
✨Tip Number 3
Showcase your projects! If you've worked on any relevant research or personal projects, make sure to highlight them during interviews. We love seeing practical applications of your skills, especially if they relate to nanoscale processes.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that are relevant to the role of Research Fellow. Highlight any experience with machine learning, deep learning models, and semiconductor manufacturing to show us you’re a great fit!
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 semiconductor fabrication. Share specific examples of your work that align with our project goals.
Showcase Your Collaboration Skills: Since this role involves working closely with fabrication engineers and supervising PhD students, make sure to mention any past collaborative projects. We want to see how you can contribute to our team dynamic!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates regarding your application status!
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 the industry, such as yield reduction and defect management. This knowledge will help you connect your skills to the role and demonstrate that you’re not just a techie but someone who understands the bigger picture.
✨Prepare for Technical Questions
Expect to face technical questions about data handling, model training, and validation processes. Be ready to discuss your experience with high-dimensional datasets and how you’ve tackled similar challenges in the past. Practising these scenarios can give you the confidence to articulate your thought process clearly.
✨Show Your Collaborative Spirit
This role involves working closely with fabrication engineers and supervising junior researchers. Be prepared to share examples of how you’ve successfully collaborated in the past. Highlight your ability to translate complex concepts into understandable terms, which is crucial for effective teamwork.