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- Tasks: Develop advanced deep learning models for semiconductor manufacturing optimisation.
- Company: Join a multidisciplinary team at the University of Southampton, leading in electronics and computer science.
- Benefits: Competitive salary, research exposure, and opportunities to publish in top journals.
- Why this job: Make a real impact on semiconductor manufacturing with cutting-edge machine learning techniques.
- Qualifications: Experience in machine learning and a passion for complex problem-solving.
- Other info: Opportunity to supervise PhD students and shape research direction.
The predicted salary is between 36636 - 44746 £ 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. Addressing this challenge requires new modelling approaches that can capture the full complexity of fabrication processes and enable optimisation before physical manufacturing begins.
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.
We are seeking a highly motivated Machine Learning Researcher to join a multidisciplinary team of fabrication engineers and AI specialists at the University of Southampton, within the School of Electronics and Computer Science, working in the group of Dr Yasir Noori. In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post-fabrication device characteristics from process parameters.
You will engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process conditions.
The models you develop will not remain confined to the research lab, but will be validated experimentally and tested at an industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation. The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact.
You will also be involved in supervising PhD students and junior researchers and play a central role in shaping the research direction of the team. Your work is also expected to contribute to the development of innovative technologies with a clear pathway to commercialisation through the spinout company Deep Fabrication, to influence how semiconductor manufacturing is approached in practice.
The role will provide you with deep exposure to nanofabrication processes, experience working with industry-relevant datasets and problems, and the opportunity 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.
This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months. To apply, please submit your CV and a cover letter outlining how your experience and interests align with the aims of the project, and provide responses to the short-listing questions.
£36,636 to £44,746 per annum
Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Southampton employer: Burwell Help Group
Contact Detail:
Burwell Help Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Southampton
✨Tip Number 1
Network like a pro! Reach out to current or former employees at the University of Southampton or in the semiconductor industry. A friendly chat can give us insights into the role and might even lead to a referral.
✨Tip Number 2
Prepare for the interview by diving deep into machine learning applications in semiconductor manufacturing. We should be ready to discuss how our skills can tackle real-world challenges, especially those related to predictive modelling and data-driven processes.
✨Tip Number 3
Showcase our passion for the field! During interviews, let’s share our excitement about applying machine learning to complex systems. We can mention any relevant projects or experiences that highlight our problem-solving skills in this area.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure our application gets noticed. Plus, we can keep an eye on other exciting opportunities that pop up in the future.
We think you need these skills to ace Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Southampton
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your experience in machine learning and semiconductor engineering. We want to see how your skills align with the specific challenges of this role, so don’t hold back on showcasing relevant projects or research!
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 manufacturing. We love seeing enthusiasm and a clear connection between your background and our project aims.
Answer Short-listing Questions Thoughtfully: Take your time with the short-listing questions. This is your opportunity to demonstrate your understanding of the complexities involved in semiconductor fabrication and how your expertise can contribute to our team. Be specific and insightful!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Burwell Help Group
✨Know Your Stuff
Make sure you brush up on the latest trends in machine learning and semiconductor manufacturing. Familiarise yourself with deep learning models and how they can be applied to fabrication processes. Being able to discuss specific techniques or recent advancements will show your passion and expertise.
✨Showcase Your Experience
Prepare to talk about your previous projects, especially those involving high-dimensional datasets or predictive modelling. Be ready to explain your role, the challenges you faced, and how you overcame them. This will demonstrate your problem-solving skills and ability to work in a multidisciplinary team.
✨Ask Insightful Questions
Come prepared with questions that show your interest in the project and the team. Inquire about the specific challenges they face in semiconductor fabrication or how they envision the integration of machine learning into their processes. This not only shows your enthusiasm but also helps you gauge if the role is the right fit for you.
✨Connect with the Team
Since this role involves collaboration with fabrication engineers and AI specialists, highlight your teamwork skills. Share examples of how you've successfully worked in teams before, and express your eagerness to contribute to a collaborative environment. Building rapport can make a big difference!