Overall, Purpose of the Job
We are looking for one Research Associate to work on the development, implementation and testing of predictive control algorithms for the optimal coordination of available multi-energy resources and network infrastructure and thus support and improve the operation of the energy networks and the performance and the sustainability of the urban energy system. Learning-based techniques (e.g., deep neural networks) will be used to automatically learn the system dynamics and the modelling errors, as well as to obtain an automatic tuning of the cost parameters/constraints or approximators of the control law. This will alleviate the modelling complexities and the online computational requirements of the control algorithms and provide them with learning, self-regulating and adaptive capabilities.
The appointed Research Associate will collaborate with researchers from other UK universities working around multi-energy networks and multi-energy technologies, focusing on their modelling, optimisation and control, within the EPSRC-funded project Supergen Energy Network Hub. This research will contribute to provide leadership, research and networking for the energy networks and systems community to grow and come together to develop a deeper understanding of the interconnected and interdependent energy network infrastructure, e.g. electricity, heat, gas, hydrogen, as well as of the urban energy systems.
Please note this post will also require UK travel to collaborating universities, as well as opportunities to present research at leading international conferences, as well as the ability to interact effectively with researchers from other disciplines and industrial partners. The post duration is likely to be extended by a time depending on the available funds and the performance.
What will you get in return:
As an equal opportunity 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 be aware that due to the number of applications we are unfortunately not able to provide individual feedback on your application.
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.talent@manchester.ac.uk
Any CV’s submitted by a recruitment agency will be considered a gift.
Enquiries about the vacancy, shortlisting and interviews:
Name: Professor Alessandra Parisio
Email: Alessandra.parisio@manchester.ac.uk
General enquiries:
Email: recruitmentservices.people@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.
