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PhD studentship: Steel Structures with AI
2 months ago
Applications are invited for a PhD studentship in the Department of Engineering.
The successful candidate will have the opportunity to work on Steel Structures and application of Machine Learning.
The proposed PhD research in Steel Structures aims to develop advanced optimisation methods for the reuse of structural steel, leveraging the power of Artificial Intelligence (AI), specifically Machine Learning (ML) algorithms and Neural Networks (NNs). This research addresses the growing need for sustainable construction practices by promoting the reusing and repurposing of steel components, which are crucial for reducing environmental impact and resource consumption.
By integrating AI techniques, the study will focus on creating predictive models and optimisation frameworks that enhance the decision-making process for reusing structural steel. ML algorithms will be employed to analyse large datasets of existing steel structures, identifying patterns and predicting the remaining strength and usability of steel elements. Neural Networks will be utilised to model complex relationships and improve the accuracy of these predictions.
The research will also explore innovative design methodologies that incorporate reused steel, ensuring structural integrity and compliance with safety standards. By automating the assessment and selection process of reusable steel components, the developed AI-based tools will significantly streamline the workflow, reduce costs, and minimise waste in construction projects.
Overall, this PhD project aims to contribute to the advancement of sustainable engineering practices, offering practical AI-driven solutions for the efficient reuse of structural steel in the built environment.
Eligibility and requirements
The candidate should have a first or upper second-class BEng/MEng (or equivalent, or higher) degree in Engineering, Computer Science or related degrees. They should demonstrate aptitude for original research.
They should possess a good understanding of AI & Maths (calculus, linear algebra, statistics and ML). The candidate should also be familiar with following Python packages (Scikit-learn, PyTorch or TensorFlow, NumPy, SciPy, Matplotlib, Pandas). A candidate who demonstrates exceptional aptitude in one or more of these areas (as evidenced, for instance, through strong academic credentials or research papers in reputable, peer-reviewed journals/conferences) may be accorded preference. Ideally, the successful candidate should have proven skills in:
Web Development
- Python frameworks (Django)
- "Vanilla" JavaScript
- HTML5
- CSS3 (Tailwind CSS)
C#
- Understanding of object-oriented programming principles, algorithms and data structures
- Developing desktop applications with .NET technologies (.NET Framework 4.8 or .NET Core)
The studentship is for 3 years and will provide an annual tax-free stipend of £21,000 and tuition fees (Home only).
Additional income: Each student may have the opportunity to earn around £2,200/year on an average (max. is around £4,300/year) through a teaching assistantship.
If you are interested in applying, and for any initial informal enquiries, please contact:
How to apply
Online applications should be submitted bu clicking the 'Apply' button, above.
For queries regarding the application process, please contact
Tuition fees (Home) and an annual tax-free stipend of £21,000
