Applications are invited for a Research Associate to work on AIOLOS: Artificial Intelligence powered framework for OnLine prOduction Scheduling.
This project is an EPSRC funded collaboration between The University of Manchester and University College London (UCL). (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/V051008/1). It 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 UCL for such development. You will also work closely with industrial partners to test the new online scheduling framework 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 Department of Chemical Engineering and Analytical Science is strongly committed to promoting equality and diversity, including the Athena SWAN charter for gender equality in higher education. The Department 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/
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: 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-od.operations@manchester.ac.uk
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.
