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Research Associate in Process Systems Engineering

The University of Manchester - Faculty/Organisation: Science and Engineering School/ Directorate: Department of Chemical Engineering & Analytical Science

Location: Manchester
Salary: £32,816 to £40,322 per annum (depending on experience)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 1st October 2020
Closes: 30th October 2020
Job Ref: SAE-015735
 

Job reference: SAE-015735

Location: Oxford Road, Manchester

Closing date: 30/10/2020

Salary: £32,816 to £40,322 per annum (depending on experience)

Employment type: Fixed Term

Faculty/Organisation: Science and Engineering

School/ Directorate: Department of Chemical Engineering & Analytical Science

Hours per week: full time

Contract Duration: starting as soon as possible until 31st October 2022

Applications are invited for a Research Associate to work on Development and Demonstration of an Effective Optimisation Approach for Large-scale Chemical Production Scheduling.

This project is an EPSRC funded collaboration between The University of Manchester and Flexciton Limited. (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T03145X/1). It is to develop a novel and effective optimisation-based method to address challenges in optimisation of large-scale chemical production scheduling. It will combine the advantages of the mathematical programming approach and a new machine learning technique, Gene Expression Programming (GEP).

You will be responsible for the development of a robust and efficient modelling framework for chemical production scheduling, the development of an efficient solution approach using gene expression programming (GEP) for systematic generation of dispatching rules, which are applicable to a variety of large-scale chemical production scheduling problems. The developed GEP-based solution approach will be then effectively combined with the mathematical programming approach for the generation of robust and high-quality dispatching rules in an offline manner, which are expected to be applicable to a variety of scheduling problems and used to generate optimal or near-optimal schedules for scheduling in an online manner. You will also closely collaborate with Flexciton Limited to test the new effective solution approach in a practical context and demonstrate the benefit.

You will have, or be about to obtain, a relevant PhD (or equivalent) in process systems engineering, computer science, operations research, industrial engineering or closely related field together with an excellent track record of international publications. Examples of field interests include advanced planning and scheduling, metaheuristics, machine learning, mathematical modelling, and optimisation. Research experience in machine learning, artificial intelligence, and optimisation are particularly preferred.

The School is strongly committed to promoting equality and diversity, including the Athena SWAN charter for gender equality in higher education. The School holds a Silver Award which recognises their good practice in relation to gender; including flexible working arrangements, family-friendly policies, and support to allow staff achieve a good work-life balance. We particularly welcome applications from women for this post. All appointment will be made on merit. For further information, please visit:

http://www.ceas.manchester.ac.uk/about-us/athena-swan/     

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: Dr Jie Li

Email: jie.li-2@manchester.ac.uk

General enquiries:

Email: hrservices@manchester.ac.uk

Technical support:

Email: universityofmanchester@helpmeapply.co.uk

Tel: 0161 850 2004

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

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