The EPSRC Centre for Doctoral Training (CDT) in Renewable Energy Northeast Universities (ReNU)is a collaborative doctoral training programme run by the Universities of Northumbria, Newcastle and Durham. In addition to undertaking an individual scientific research project at one of the three partner Universities, doctoral candidates will engage with added value training opportunities, for example in business, innovation and internationalisation through a 4-year training programme that has been designed to maximise the benefits of a cohort approach to doctoral training. The start date is 1st October 2022.
Due to global environmental concerns, the UK has set an ambitious target of net-zero carbon emissions by 2050. To achieve this target, future power systems will integrate more renewable energy sources and connect more distributed energy resources. Smart local energy systems, which facilitate more renewable energy penetration and integrate the energy use, generation and storage locally, play an important role to help save costs and decarbonise the energy sector. However, there are multiple uncertainties in smart local energy systems that could compromise their full potentials, including those from the intermittency of renewable energy sources, more interactions with electric vehicles and the ever-increasing dependence on cyber systems for collecting data and coordinating operations. Efficient operations will require data intensive techniques and optimisation methods to enable the dynamic balance of power supply and demand.
This project aims at addressing challenges for future smart local energy systems and will explore data intensive techniques to develop practical planning and optimization methods considering realistic cyber network limitations. Data from measurement/metering devices can be used to develop digital twins, building on which optimization methods will be developed to minimize the operational cost or carbon emissions. Data-driven digital twins can be integrated with artificial intelligence (AI), where data-intensive AI tools will be developed to enable more efficient decision-making and planning. This project will also exploit the interrelation of physical energy systems and cyber systems, and address the challenges caused by the high dependence on cyber systems.
We are recruiting one PhD student to contribute to the project. The candidates are expected to have solid knowledge in Energy, Electrical/Electronic Engineering, AI & Machine Learning, demonstrably experiences of programming/simulations, strong analytic skills, and excellent communication skills, both written and oral, in English.
This project is supervised by Dr Jing Jiang. For informal queries, please contact [Email Address Removed].
The application closing date is 6 June 2022. Please note that interviews, should they be arranged, will be online rather than in person due to COVID-19.
Eligibility and How to Apply:
Please note eligibility requirement:
· Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
· Appropriate IELTS score, if required. You can apply without proof of English Language proficiency however it is preferable that candidates have met the English Language requirements by the application deadline date.
· Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere or if they have previously been awarded a PhD.
For further details of how to apply, entry requirements and the application form, see
Please note that applications must include all of the following to be considered:
- A research proposal of approximately 1,000 words (not a copy of the advert), and include the advert reference (e.g. ReNU22-R/…).
- You must upload with your application at least 2 reference letters
Deadline for applications: 6 June 2022
Start Date: 1 October 2022
Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.
Home and International students (inc. EU) are welcome to apply. The studentship is available to Home and International (including EU) students and includes a full stipend at UKRI rates (for 2022/23 full-time study this is £16,602 per year) and full tuition fees. Also significant additional funding to cover research costs and local, national and international travel (conferences and exchanges).
Applicants should be aware of the following additional costs that you may incur as these are not covered by the studentship.
· Immigration Health Surcharge https://www.gov.uk/healthcare-immigration-application
· If you need to apply for a Student Visa to enter the UK, please refer to the information on https://www.gov.uk/student-visa. It is important that you read this information very carefully as it is your responsibility to ensure that you hold the correct funds required for your visa application otherwise your visa may be refused.
· Check what COVID-19 tests you need to take and the quarantine rules for travel to England https://www.gov.uk/guidance/travel-to-england-from-another-country-during-coronavirus-covid-19
· Costs associated with English Language requirements which may be required for students not having completed a first degree in English, will not be borne by the CDT. Please see individual adverts for further details of the English Language requirements for the university you are applying to.
Note that 1 offer of a PhD place will be made for the ReNU CDT projects with the reference number 'ReNU22-R/…' advertised by Northumbria University.
* please note: to be classed as a Home student, candidates must meet the following criteria:
• Be a UK National (meeting residency requirements), or
• have settled status, or
• have pre-settled status (meeting residency requirements), or
• have indefinite leave to remain or enter.
If a candidate does not meet the criteria above, they would be classed as an International student. Further information about how EPSRC classifies international fee status please see Annex B of https://www.ukri.org/wp-content/uploads/2021/08/UKRI-170821-TrainingGrantTermsConditionsGuidance-Aug2021.pdf