Applications are invited for an EPSRC-funded Studentship in Data Analytics for the Internet of Things.

Industrial supply chains are emergent chains of manufacturers that outsource production to each other. These chains are subject to a variety of disruptions that impact their function and performance. With the introduction of Internet of Things (IoT) based technologies it is now possible to create a system in which the disruption of a supplier can be detected and communicated to its dependent manufacturers, who can then organise internal production accordingly. However, there is a lack of automated solutions that allow such corrections to take place at each part of an interdependent supply chain. Such a solution would need to take input from and negotiate with all dependent manufacturers. Currently every supply chain member responds to disruptions in a reactive, independent way, buying excessive stock, which increases material waste. Coupling IoT data and Machine learning techniques could help coordinate the supply chains and reduce response time by automating decisions, so that demand and supply matches more closely.

This project aims to couple IoT and machine learning based approaches for coordinating emergent disruption response strategies in industrial systems.

The project is expected to lead to:

An understanding of the types of IoT data that can help coordinate disruptions in inter-dependent industrial systems;

• An understanding of machine learning approaches could work in disrupted industrial systems with regards to response time and optimal decision making.
• A self-organising approach to disruption management in inter-dependent industrial systems.
• Practical case studies with industry

For further information contact Dr A Brintrup ([email protected])

Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree (and preferably a Masters degree) in an Engineering or a related subject. An understanding of Artificial Intelligence based techniques, Data Analytics, in addition to software programming experience will be advantageous. A creative approach to solution finding, and willingness and capacity to learn is critical.

EPSRC funded studentships are available for Home and EU students. Home students and certain EU students will receive a full studentship including fees and Maintenance. EU students will receive a fees only award. Details on eligibility can be found of EPSRC Web site: https://www.epsrc.ac.uk/skills/students/help/eligibility/ Overseas students are not eligible and should not apply.

Applications should be made on-line via the Cambridge Graduate Admissions Office before the deadline: http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/ with Dr Brintrup identified as the potential supervisor.

The University values diversity and is committed to equality of opportunity.

source:jobs.ac.uk