At a Glance
- Tasks: Develop advanced machine learning models for nanoscale semiconductor manufacturing.
- Company: Join a leading research team at the University of Southampton.
- Benefits: Gain hands-on experience, publish research, and collaborate with industry leaders.
- Other info: Opportunity to supervise PhD students and shape research direction.
- Why this job: Make a real impact in semiconductor manufacturing with cutting-edge technology.
- Qualifications: Strong background in machine learning and interest in semiconductor engineering.
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 Hampshire employer: University of Southampton
The University of Southampton is an exceptional employer, offering a dynamic and collaborative work environment where innovation thrives. As a Research Fellow in Machine Learning for Nanoscale Semiconductor Manufacturing, you will have the unique opportunity to engage with cutting-edge technology and industry partners, while contributing to impactful research that shapes the future of semiconductor fabrication. With a strong commitment to employee growth, diversity, and inclusion, the university fosters a culture of continuous learning and professional development, making it an ideal place for those seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Hampshire
✨Tip Number 1
Network like a pro! Reach out to people in the semiconductor and machine learning fields on LinkedIn. Join relevant groups, attend webinars, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to machine learning and semiconductor manufacturing. Use platforms like GitHub to share your code and results. This gives potential employers a taste of what you can do before they even meet you.
✨Tip Number 3
Prepare for those interviews! Research common questions in the field of machine learning and semiconductor processes. Practice explaining complex concepts in simple terms, as you’ll need to communicate effectively with both technical and non-technical team members.
✨Tip Number 4
Apply through our website! We’re always on the lookout for passionate individuals like you. Make sure to tailor your application to highlight your experience with deep learning models and data-driven manufacturing. Let’s make an impact together!
We think you need these skills to ace Research Fellow – Machine Learning for Nanoscale Semiconductor Manufacturing in Hampshire
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-driven manufacturing to catch our eye!
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 fabrication. Share specific examples of your work that align with the responsibilities outlined in the job description.
Showcase Your Research Experience:Since this role involves working with complex datasets and developing predictive models, be sure to detail any relevant research projects you've been involved in. Discuss your methodologies and outcomes to demonstrate your capability in this area.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
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 architectures and how they apply to fabrication processes. Being able to discuss specific models or techniques will show your passion and expertise.
✨Showcase Your Experience
Prepare to talk about any relevant projects you've worked on, especially those involving high-dimensional datasets or real-world applications of machine learning. Highlight your problem-solving skills and how you've tackled complex challenges in previous roles.
✨Engage with the Team
Since this role involves collaboration with fabrication engineers, be ready to discuss how you would work within a multidisciplinary team. Show enthusiasm for teamwork and be prepared to share examples of how you've successfully collaborated in the past.
✨Ask Insightful Questions
Prepare thoughtful questions about the research direction of the team and the specific challenges they face in semiconductor manufacturing. This not only shows your interest but also helps you gauge if the role aligns with your career goals.
