PhD Classic Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, in-line with the Research Council values, which comprises a tax-free stipend paid monthly (2024/5 - £19,237) per year and a Full Time Fee Scholarship for up to 3 years, subject to you making satisfactory progression within your PhD.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
23:59 on Tuesday 30th April 2024 for a start date of the 2nd September 2024.
How to Apply
To apply, please complete the project proposal form, ensuring that you quote the project reference, and then complete the online application where you will be required to upload your proposal in place of a personal statement as a pdf document.
You will also be required to upload two references, at least one being an academic reference, and your qualification/s of entry (Bachelor/Masters certificate/s and transcript/s).
Project Title: Smart Building Process Connectivity (SBPC): Enhancing Operational Efficiency through Data-Driven Algorithms
Project Lead: Dr Gerald Feldman Gerald.Feldman@bcu.ac.uk
Reference: 11 SBPC
Project Description
This research aims to enhance operational efficiency using data driven algorithms within smart buildings. The research includes:
- A Comprehensive review of process connectivity, smart building frameworks, operational and maintenance data, and stakeholder requirements.
- Development of simulation models through use cases to demonstrate the concept.
- Collected simulation data used to drive novel data-driven algorithms.
- Application of algorithms for operational optimization, efficiency, and predictive maintenance in smart buildings.
Anticipated Findings and Contribution to Knowledge
The anticipated findings and contribution to this project are as follows:
- An improved process connectivity model for smart buildings through an in-depth review of related works. The review can include process connectivity, smart building frameworks, operational and maintenance data, and stakeholder requirements.
- Utilization of simulation tools (i.e., MATLAB/Simulink, Network Simulator 2, Python), for developing simulation model for smart buildings based on literature review insights.
- Development of novel data-driven algorithms for (a) optimising the operational efficiency; and (b) predictive maintenance of the smart buildings.
- Evaluate the system performance and demonstrate the efficiency of the integrated system among operational and non-operational requirements (i.e., energy and stakeholder).
Person Specification
- Seeking a motivated committed candidate preferably with a master’s in computing.
- Strong analytical skills, and effective communication and collaboration skills, paired with a passion for smart building technologies, are essential for translating research into practical solutions.
- Proficiency in simulation tools (e.g., Simulink, Network Simulator 2) is desirable.