Research Associate in Artificial Intelligence Powered Framework for OnLine Production Scheduling

Job reference: SAE-029022
Salary: £37,174 - £45,413 per annum, depending on relevant experience
Faculty/Organisational Unit: Science and Engineering
Location: Oxford Road
Employment type: Fixed Term
Division/Team: Department of Chemical Engineering
Hours Per Week: 35 hours
Closing date (DD/MM/YYYY): 01/07/2025
Contract Duration: 11 months
School/Directorate: School of Engineering

Overall purpose of the job:

This position is to deliver a next generation autonomous online scheduling framework in response to different types of disruptions in the chemical manufacturing industry using machine learning techniques. You will be responsible for the evaluation of energy consumption for industrial data reconciliation and preparation of process scheduling models, quantification of different types of uncertainty and the development of data-driven autonomous techniques for online scheduling. You will collaborate seamless with academics from University College London for such development. You will also work closely with the industrial partners to test the new online scheduling framework in a practical context and demonstrate the benefit.

The position requires strong expertise in process systems engineering, mathematical modelling, optimisation, machine learning, and artificial intelligence.

What you will get in return:

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers

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

Hybrid working arrangements may be considered.

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

Any recruitment enquiries from recruitment agencies should be directed to People.Recruitment@manchester.ac.uk. Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Dr Jie Li

Email: jie.li-2@manchester.ac.uk

Or

Name: Dr Dongda Zhang

Email: Dongda.zhang@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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