UNIVERSITY OF LINCOLN

Post Doctoral Research Associate in Smart Manufacturing and AI

Location
Lincoln, United Kingdom
Salary
£33,797 per annum
Posted
23 Feb 2021
End of advertisement period
28 Feb 2021
Ref
COS806
Contract Type
Fixed Term
Hours
Full Time

School of Engineering

Location:  Lincoln
Salary:   From £33,797 per annum
These are full time posts of 1 FTE, Fixed term for 6 months from start date
Closing Date:   Sunday 28 February 2021
Interview Date:  Thursday 18 March 2021
Reference:  COS806

The University of Lincoln is seeking to appoint multiple Postdoctoral Research Associates to join the School of Engineering and the Lincoln Centre for Autonomous Systems (LCAS). The posts will be for 6 months and funded by the Innovate UK. The project comes under the framework of “Manufacturing made smarter: digital supply chain, feasibility studies’’. The overall aim is to develop innovative AI-based technologies, to make manufacturing smarter in the future. A summary of the project is here below:

Project Description

Process and quality planning are prerequisites for producing high-quality products at competitive costs. Today ´s manufacturing relies on old-fashioned document-centric methods that are ineffective; valuable time is mostly spent on document creation and document management rather than innovation and process improvement. This project aims to optimise the process and quality planning through a model-based approach. A cloud-based data infrastructure will be developed and tested for use-cases from the Aerospace and Space systems manufacturing.

About the candidate

We are looking for enthusiastic and passionate individuals to join our research team at the School of Engineering. The post holder is expected to have completed (or be close to completing) a PhD in Computer Science, Systems Engineering or similar fields. The post holder will be required to model various processes across the supply chain and therefore, previous experience of applying deep learning techniques to model sequential data and in particular time-series is crucial. The candidates must have extensive experience with Python and in particular the TensorFlow library. 

Informal enquiries about these posts can be made to Prof Mini C. Saaj (msaaj@lincoln.ac.uk) or Dr Sepehr Maleki (smaleki@lincoln.ac.uk).