The advent of Industry 4.0 has ushered in a new era of manufacturing, marked by the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins. This research project will investigate the methodologies and technologies necessary to create an accurate and dynamic digital twin that reflects the real-time status of a manufacturing process, thereby enabling enhanced decision-making, predictive maintenance, and improved production efficiency.
This project will be jointly supervised by:
Prof Prof Jonathan Corney j.r.corney@ed.ac.uk
Dr Matjaz Vidmar matjaz.vidmar@ed.ac.uk
Smart Products Made Smarter
The PhD project forms part of a larger Prosperity Partnership Programme, Smart Products Made Smarter, a collaboration with Heriot-Watt University, University of Edinburgh and Leonardo.
We are pleased to invite applications for a PhD studentship to work as part of a leading team of experts. This studentship will be supported by an enhanced stipend of £20,716 per year over 3.5 years.
This grant, sponsored by the EPSRC, is a collaboration between academia and Leonardo. There are currently PhD opportunities available to work on diverse topics as part of this collaborative team. The work will involve strong links with industry.
The research addresses a broad range of challenges. These challenges exemplify future product lifecycle management from smart concept, design, development and manufacture to enhanced end-user capability, united by a common digital thread to enable smarter products to be made smarter. Each challenge area has clearly identified initial research themes and associated research challenges to be addressed and these are indicated below:
Challenge 1 (C1) the Making challenge: To create new hybrid manufacturing processes, that combine multiple Additive Manufacturing (AM) process with precision machining and coating processes to create components that disrupt the traditional functional trade-offs of Size, Weight and Power (SWaP) through techniques such as varying the material properties within a part and harnessing the digital production of optical components.
Challenge 2 (C2) the Manipulation challenge: To create new handling processes that fully exploit the digital data flows which define custom components whose shape and functionality is tailored to production by dexterous, highly adaptable robots that are programmed dynamically.
Challenge 3 (C3) the Computation challenge: To create new signal processing & machine learning methodologies that enable intelligent, digital & connected sensor products while mitigating the data deluge from the multiple sensors produced by Leonardo operating across the EM spectrum.
The themes represent areas that could form the basis of your PhD. These PhD positions offer great flexibility and we welcome the opportunity to explore other ideas & themes.
Please note that this advert will close as soon as a suitable candidate is found.
Further Information:
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Closing Date:
Principal Supervisor:
Assistant Supervisor:
Eligibility:
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Please note that as this is a defence based project, only UK/EU students are eligible to apply. International applicants are not eligible.
Further information on English language requirements for EU/Overseas applicants.
Funding:
Tuition fees + stipend are available for applicants who qualify as:
- a UK applicant
- an EU applicant (International/non EU students are not eligible)
Funding is available through EPSRC Prosperity Partnership Programme. As this is a defence related project there are nationality restrictions (see above).
Informal Enquiries:
Prof Jonathan Corney, j.r.corney@ed.ac.uk
