Research Associate in Artificial Intelligence Powered Framework for OnLine Production Scheduling

The University of Manchester; Manchester; On-site; GBP 30,000 - 40,000; Full time

Job summary

The University of Manchester is seeking a Research Associate to lead the development of an AI-driven scheduling framework that addresses disruptions in chemical manufacturing. This position calls for strong skills in process systems engineering and machine learning, alongside collaboration with industry partners. You will be involved in practical testing and further development of innovative scheduling solutions.

Benefits

Fantastic market leading Pension scheme

Excellent employee health and wellbeing services

Exceptional starting annual leave entitlement

Additional paid closure over the Christmas period

Local and national discounts at major retailers

Qualifications

• Strong expertise in process systems engineering and machine learning.
• Experience with mathematical modelling and optimisation.
• Ability to work collaboratively with academic and industrial partners.

Responsibilities

• Develop an autonomous online scheduling framework for chemical manufacturing.
• Evaluate energy consumption for data reconciliation and scheduling models.
• Collaborate with partners for practical testing of the scheduling framework.

Skills

Process systems engineering

Mathematical modelling

Optimisation

Machine learning

Artificial intelligence

Education

Relevant degree in Engineering or Computer Science

Job description

Research Associate in Artificial Intelligence Powered Framework for OnLine Production Scheduling

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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.

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 [e-mail]. 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: [e-mail]

Or

Name: Dr Dongda Zhang

Email: [e-mail]

General enquiries:

Email: [e-mail]

Technical support:

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

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

Please see the link below for the Further Particulars document which contains the person specification criteria.

Seniority level

• Seniority level

  Entry level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Higher Education

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