AI Technology for Smart Labelling and Root Cause Analysis in a Semiconductor Wafer Manufacturing Process

The Intelligent Systems Research Centre (ISRC) vision is to develop a bio-inspired computational basis for Artificial Intelligence. Our mission is to understand how the brain works at multiple levels, from cells to cognition and apply that understanding to create realistic models and construct technologies that solve the complex issues that face people, industry and society. To accomplish our mission we use a variety of research strategies that include big data and machine learning, brain imaging and neural interfacing, human-computer interaction and robotics.

Bioinspired artificial intelligence and machine learning have spurred an era of data analytics that has the potential to revolutionise the way we work and live and many industries and companies are realising that the data they collect have substantial value but the data is often noisy, unstructured, and non-stationary and thus complex, requiring advanced learning capabilities to learn from the data to create intelligent machines and devices that act autonomously to improve products, processes, services and productivity. At the ISRC we develop expertise in AI and data analytics to address such challenging data.

In this project the successful PhD applicant will work within the Smart Nano Manufacturing Northern Ireland Consortium (https://www.smartnanoni.com ). SmartNanoNI is a £40 million Northern Ireland consortium, collaborating to develop advanced prototypes and smart manufacturing methods to make factories more intelligent and facilitate fabrication of Smart Nano products. It will capitalise on the advances and innovations associated with industrial digitisation, data availability, acceleration in AI capability, robotics and automation. The project will involve extensive collaboration with Seagate technologies and aimsto develop and apply AI approaches to address challenges associated with for example optimisation, routing and scheduling to reduce product development cycle times, predictive analytics for tool matching and understanding tool health, extracting and classifying information from complex, unstructured text-based datasets and robotic automation. This project offers an exciting opportunity for PhD research in AI/machine learning, digital twins, and addressing challenging data analytics and machine intelligence problems.

Working alongside the supervisory team at the ISRC, data scientists and domain experts within the industry partners, the PhD Researcher will develop and/or apply leading AI solutions including deep learning with long-term short-term memory, attention and transformer-based algorithms and/or multi-objective co-evolutionary evolutionary optimisation problems to address some of the major challenges associated with smart manufacturing. The PhD position will also involve curating data through gaining domain knowledge as well as defining and refining project goals and translating AI approaches across various industry sectors to address industry-led data analytics challenges. The PhD will contribute to planning of research, managing and analysing data, developing software and technologies for experiments and prototyping real-time AI solutions, and will be expected to contribute to reporting results and publishing the results of the research in high impact journal publications.

The PhD opportunity will enable the successful candidate to gain that expertise and to push the boundaries on the state-of-the-art, and apply their knowledge to develop solutions to challenging industry led problems that will have a significant short-term impact.

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A comprehensive and articulate personal statement
• A demonstrable interest in the research area associated with the studentship

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

This project is funded by:

• UKRI Strength in Places Fund – Smart Nano-Manufacturing

This scholarship will cover tuition fees and provide a maintenance allowance of £19,237 per annum for three years (subject to satisfactory academic performance).

This scholarship is open to both home and overseas applicants.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.