Heriot-Watt University 2022 James-Watt Scholarship Program for Ph.D Students

A funded Ph.D. opportunity is available under the James-Watt scholarship program at Heriot-Watt University, Edinburgh, UK, to develop data-driven and data science methods for the decarbonisation of energy and water systems. It is well known that the Built Environment contributes around 40% of all carbon emissions globally, and that, of this, energy and water consumption make up a significant proportion. Many opportunities therefore exist to help rectify this position and to create meaningful outcomes to support decarbonisation of the construction sector. This research focuses on the underlying physics of heat dissipation and water flow in buildings, and on the analysis of related demand patterns and associated data using Artificial Intelligence (AI) and Machine Learning (ML) techniques. Here, statistical and deep learning methods will be used to identify the optimum method to facilitate analysis so as to allow improved decision-making and define enhanced pathways to Net Zero energy and water usage within buildings. As well as developing and assessing appropriate techniques, the work will draw comparisons with engineering and simulation-based predictions of water and energy consumption. The focus of the research will be on consumption at the building scale, but they envisage a future interface with urban scale demand models.

Heriot-Watt University 2022 James-Watt Scholarship Program for Ph.D Students
A funded Ph.D. opportunity is available under the James-Watt scholarship program at Heriot-Watt University, Edinburgh, UK, to develop data-driven and data science methods for the decarbonisation of energy and water systems. It is well known that the Built Environment contributes around 40% of all carbon emissions globally, and that, of this, energy and water consumption make up a significant proportion. Many opportunities therefore exist to help rectify this position and to create meaningful outcomes to support decarbonisation of the construction sector. This research focuses on the underlying physics of heat dissipation and water flow in buildings, and on the analysis of related demand patterns and associated data using Artificial Intelligence (AI) and Machine Learning (ML) techniques. Here, statistical and deep learning methods will be used to identify the optimum method to facilitate analysis so as to allow improved decision-making and define enhanced pathways to Net Zero energy and water usage within buildings. As well as developing and assessing appropriate techniques, the work will draw comparisons with engineering and simulation-based predictions of water and energy consumption. The focus of the research will be on consumption at the building scale, but they envisage a future interface with urban scale demand models.