|Salary:||£33,309 to £36,382|
|Placed On:||6th September 2021|
|Closes:||21st September 2021|
Strathclyde Business School
Term: Fixed (36 months)
Become part of this exciting multi-disciplinary three-year EPSRC funded project to improve industrial operations. The successful applicant will join the department of Management Science in the University of Strathclyde and work in a team with academics at the Universities of Edinburgh and Napier as well as industrial partners Babcock, Simul8 and Ubisense. Through this collaborative project, we will develop methods to identify opportunities for improvements in efficiency, productivity and sustainability.
The rapid advance of digital sensing technologies is making the real time recording of activities in a manufacturing environment both practical and affordable. However, the availability of diverse, real time data about movement and activity does not automatically help engineers manage the complex, dynamic environments typical of modern industrial operations. To do this they need tools that support their interpretation of constantly changing data in ways that enhance productivity and sustainability.
Motivated by this need, this project will investigate if the forms of probabilistic networks that have been employed to generate computational models from location tracking data in other contexts (e.g. vehicles movements in traffic models and the daily routines of individuals in domestic environments) can be extended to work with multiple forms of industrial activity data recorded on a factory floor. Such a model would allow diverse signals of manufacturing activity (e.g. material transport, staff movement, vibration, electrical current and air quality etc.) to be used to infer the behaviour of an industrial workplace and generate quantitative measures that support decisions, which affect sites’ production and sustainability performance.
As a Research Associate, under the general guidance of a research leader, you will play a lead role in relation to this project contributing to the development of new methods for analysis. You will write up research work for publication, individually or in collaboration with colleagues, and disseminate the results via peer reviewed journal publications and presentation at conferences. You will join external networks to share information and ideas, collaborate with colleagues to ensure that research advances, participating in initiatives, which establish research links with industry. You will supervise student projects and provide advice to students.
To be considered for the role, you will be educated to a minimum of PhD level in an appropriate discipline, or have significant relevant experience in addition to a relevant degree. You will have sufficient breadth or depth of knowledge in data analytic methods and a developing ability to conduct individual research work and to disseminate results. You will have an ability to plan and organise your own workload effectively and an ability to work within a team environment. You will have excellent interpersonal and communication skills, with the ability to listen, engage and persuade, and to present complex information in an accessible way to a range of audiences.
Please note this is a fixed term post with an expected duration of 36 months.
Formal interviews for this post are expected to be held on Friday, 1 October 2021.
Informal enquiries about the post can be directed to Professor John Quigley, email@example.com
Click here for full details.
Type / Role: