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KTP Associate Vision Guided Robotics Specialist

The University of Manchester - Department of Electrical & Electronic Engineering

Location: Manchester
Salary: £33,309 to £40,927 per annum, depending on relevant experience
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 22nd June 2022
Closes: 19th July 2022
Job Ref: SAE-019246
 

Job reference: SAE-019246

Location: Oxford Road, Manchester

Closing date: 19/07/2022

Salary: £33,309 to £40,927 per annum, depending on relevant experience

Employment type: Fixed Term

Faculty/Organisation: Science and Engineering

Division: Department of Electrical & Electronic Engineering

Hours per week: Full time

Contract Duration: From 1 September 2022 for 30 months

School / Directorate: School of Engineering

This is an exciting opportunity for an ambitious researcher or engineer with passion about environment and ability and confidence to manage a Knowledge Transfer Partnership (KTP) project with a highly innovative waste management company Kenny Waste Management (KWM) and The University of Manchester. The project provides an excellent opportunity to develop cutting edge image processing and recognition system and robotic pick-and-place system for a ground-breaking application in recycling industries that will have an unprecedented environmental impact and will dramatically improve the efficiency, safety, and quality of waste recycling operations. The technology will transform the way in which waste recycling plants process waste, recovering valuable materials, and reducing landfill delivering both economic and environmental benefits.

After a successful feasibility study on the project, The University of Manchester and KWM are looking to recruit a high calibre individual with a background in vision system or image recognition and/or robotic engineering as the KTP Associate to undertake this 30-month project which has an overall aim of developing an advanced vision guided robotic pick-and-place system for material recovery facilities, especially KWM recycling operations.

The position will provide the successful candidate with a unique opportunity to learn the state-of-the-art deep learning and image processing techniques and applying them to one of the most challenging image recognition problems and also to one of the challenges facing waste recycling and environmental industries. KWM provides a perfect platform for experimenting and trialling developed the cutting-edge technology. 

Candidates will require a recently obtained minimum of a 2:1 degree (or equivalent) in computer science or robotics or electronic engineering. Master or PhD graduates are very welcome.

This post is funded through a Knowledge Transfer Partnership (KTP) award, a UK Government flagship scheme intended to promote sustained and mutually beneficial relationships between universities and industry. There are professional development budget and opportunities as well as funded training courses on business, environment protection and project management.

The KTP associate will be an employee of the University of Manchester but will be primarily based at KWM, Manchester, with regular supervision meetings with the University supervisory team and industrial supervisors of KWM and will use the facilities and resources of both organisations. 

Due to the nature of the funding, KTP Associates who have already completed a KTP are not eligible to apply.

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

Our University is positive about flexible working – you can find out more here

Blended working arrangements may be considered

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Enquiries about the vacancy, shortlisting and interviews:

Name: Prof. Hujun Yin

Email: hujun.yin@manchester.ac.uk

General enquiries:

Email: People.recruitment@manchester.ac.uk

Technical support:

https://jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

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