Research Fellow in Integrated Intelligent Diagnostic System (EPSRC – EVES) (Fixed Term) (COS510A) | Lincolnshire Jobs | Higher Education Recruitment | Jobs from Keystone Employment Group

Research Fellow in Integrated Intelligent Diagnostic System (EPSRC – EVES) (Fixed Term) (COS510A)

Reference:
KSJ70321
Location:
Hours per Week:
35
Job Type:
Sector:

Job description

This is a fixed-term appointment for 12 months, funded by EPSRC. 

Dependent upon the outcomes, the potential of an extension supported by Siemens Industrial Turbomachinery Ltd. exists after a successful completion of the post. 

Applicants are invited for a Research Fellow position at the School of Engineering, University of Lincoln, to work on a project “Evolutionary Virtual Expert System” from July 2018 (or as soon as possible thereafter), funded by EPSRC (EP/R029741/1). This project aims to design, implement and evaluate a prototype of an Integrated Intelligent Diagnostic System, which comprise novel signal-processing and modelling techniques, as well as elements derived from industrial expert knowledge.

The Lincoln School of Engineering was the first wholly new engineering School to be opened in the UK for decades. The rationale for the School, in terms of teaching and research, was conceived in collaboration with Siemens Industrial Turbomachinery Ltd., Lincoln. The University of Lincoln is a Siemens Global Principal Partner University, one of only 4 in the UK, and one of 16 worldwide. 

This project is built upon the ongoing collaboration between the School of Engineering and Siemens Industrial Turbomachinery Ltd., Lincoln, who will provide access to expert engineers' experience and real industrial data, as well as opportunities for implementation and evaluation of the system on their existing software platform. Moreover, there are established works from the existing team members, which will help accelerate the process of the system implementation.

The post-holder's primary tasks will be (1) to gather and contribute to the research of novel signal processing-based fault diagnostic techniques; (2) to collaborate and assist the testing of the novel model-based fault diagnostic techniques; (3) to investigate and integrate the expert knowledge in the system; and (4) to prototype and evaluate the developed diagnostic system using real industrial cases. Additional tasks include research project management and reporting to the line manager and industrial partners. 

The applicant should possess a PhD (or be close to obtaining) in Engineering, Computer Science, Maths and Physics or a related area. The applicant should have experience in Fault Diagnostics or a related subject, using signal processing and machine learning techniques. Knowledge in principles of gas turbines would be an advantage, but not essential, since gas turbine training will be given during the appointment. The selected candidate should be able to present good programming skills in at least some of the following programming languages, e.g. Matlab, C#, R, Python, etc. Candidate should also have good communication skills, and be willing to undertake some of the research work in an industrial environment.

Informal enquiries about the post can be made to the Principal Investigator Dr Yu Zhang (email: yzhang@lincoln.ac.uk).

Closing Date: 28 Aug 2018
Category: Research

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