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- Tasks: Develop advanced machine learning models for semiconductor manufacturing optimisation.
- Company: Join a leading research team at the University of Southampton.
- Benefits: Gain hands-on experience, publish research, and collaborate with industry leaders.
- Why this job: Make a real impact in semiconductor manufacturing with cutting-edge technology.
- Qualifications: Strong background in machine learning and data analysis.
- Other info: Opportunity to shape research direction and mentor junior researchers.
The predicted salary is between 35000 - 45000 £ 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 spin‑out 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.
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Eastleigh employer: University of Southampton
Contact Detail:
University of Southampton Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Eastleigh
✨Tip Number 1
Network like a pro! Reach out to people in the semiconductor and machine learning fields. Attend relevant events, webinars, or even local meetups. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to machine learning and semiconductor manufacturing. This could be anything from code snippets to case studies. It’s a great way to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by diving deep into the specifics of the role. Brush up on your knowledge of nanoscale processes and how machine learning can optimise them. Being able to discuss these topics confidently will set you apart from other candidates.
✨Tip Number 4
Don’t forget to apply through our website! We’re always looking for passionate individuals who want to make an impact in the field. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Eastleigh
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 in Machine Learning for Nanoscale Semiconductor Manufacturing. Highlight any experience with deep learning models, semiconductor processes, or data analysis 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 machine learning and semiconductor manufacturing. Share specific examples of your work that align with the job description, and let us know how you can contribute to our team.
Showcase Your Projects: If you've worked on relevant projects, whether academic or personal, make sure to include them in your application. We love seeing practical applications of your skills, especially if they involve complex datasets or predictive modelling!
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 serious about joining our team at StudySmarter!
How to prepare for a job interview at University of Southampton
✨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 real-world applications of machine learning. Highlight any experience you have with microscopy, spectroscopy, or electrical performance measurements, as these are directly relevant to the role.
✨Ask Insightful Questions
Come prepared with questions that demonstrate your interest in the team and the project. Inquire about the current challenges they face in semiconductor fabrication or how they envision the integration of machine learning into their workflows. This shows you're not just interested in the job, but also in contributing to their success.
✨Emphasise Collaboration
Since this role involves working closely with fabrication engineers and supervising junior researchers, highlight your teamwork skills. Share examples of how you've successfully collaborated in multidisciplinary teams and how you can bridge the gap between machine learning and engineering.