Application Deadline: 31 January 2026
Details
Efficient use of water and energy is crucial for the economic growth of most countries, but these systems have traditionally been planned and managed separately. While this separation can make the systems easier to handle, it often leads to unnecessarily expensive and inefficient decisions, as it overlooks important water–energy interactions. For example, water is needed to produce electricity, and electricity is needed to pump and treat water.
Such inefficient, separate treatment of water and energy is no longer justifiable in the face of current climate concerns, where we are confronted with costly challenges in meeting increasing water demands and integrating large volumes of renewable energy sources.
To address these challenges, new water–energy modelling tools are being developed to identify win-win situations (instead of trade-offs) to improve affordability, resilience and sustainability. However, existing tools are currently too complex and can only consider a limited number of options. Moreover, they tend to model the impacts of renewable energies (e.g. solar and wind) only at a high level, overlooking how their intermittent nature can impact the dynamics of the energy system.
Researchers at the University of Manchester (UoM) are tackling this issue by developing AI-based tools that can evaluate hundreds, or even thousands, of options, thereby enabling better (e.g. cheaper) decisions. More importantly, these tools offer exciting new opportunities to capture the dynamic behaviour of energy systems and to more accurately assess the impacts of renewable energy sources.
This PhD project will focus on further developing UoM’s AI tools, enhancing their ability to capture the fast and complex dynamic effects caused by renewables. The student will create new, efficient tools to support intelligent water–energy decisions that align with climate goals and inform investment plans for a cleaner, more resilient energy future. To this end, the student will receive expert guidance and key training in areas such as water and energy systems, AI tools, and energy system dynamics.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply: To be considered for this project you’ll need complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for FSE Bicentenary PhD. If you don’t already have an applicant account, please follow the instructions here.
When applying, you’ll need to specify the full name of this project, the name of your proposed supervisor/s, details of your previous study, and names and contact details of two referees. You also need to provide a Personal Statement describing the motivation to apply to the project and your CV. Your application cannot be processed without all of the required documents, and we cannot accept responsibility for late or missed deadlines where applications are incomplete.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester, and are at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
Eligibility: Applicants are expected to hold (or about to obtain) a minimum upper second-class undergraduate honours degree (2:1) or equivalent in electrical or civil engineering. Research experience in electricity networks, water systems or metaheuristics (e.g., evolutionary algorithms, neural networks or machine learning) is desirable.
FSE_Bicentenary
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently ÂŁ20,780/annum) and up to a ÂŁ5k/annum research training support grant for the full duration of the 4-year programme.
