At a Glance
- Tasks: Develop a digital twin framework for railway bridge management and anomaly detection.
- Company: University of Leeds, offering a supportive research environment.
- Benefits: Tax-free maintenance grant of £19,237 per year plus additional top-up for eligible graduates.
- Other info: Join a dynamic team with opportunities for impactful research and career development.
- Why this job: Contribute to sustainable transport solutions and innovative engineering practices.
- Qualifications: First or Upper Second Class UK Bachelor in computer science, civil engineering, or related fields.
The predicted salary is between 19237 - 22237 £ per year.
Funding EPSRC Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. An additional top up of £3,000 per year for 3.5 years is also available to previous graduates of the University of Leeds.
Lead Supervisor’s full name & email address: Professor Vasilis Sarhosis v.sarhosis@leeds.ac.uk
Co-supervisor name(s): Professor David Connolly d.connolly@leeds.ac.uk, Professor Anthony Cohn a.g.cohn@leeds.ac.uk
Project summary: Recently, with the increasing global demand for mass transportation and freight, the maintenance of existing transport infrastructure has become important. Therefore, it is essential that railway infrastructure is reliable, cost-efficient, and provides a sustainable transportation mode. However, most of our existing railway infrastructure is ageing and requires continuous monitoring to keep them in service, which requires significant cost. Moreover, these structures are subjected to heavier axle loads, faster train speeds, and greater frequencies of trains, which have resulted in rapid deterioration over time. Apart from that, factors such as extreme variations in temperature, heavy rainfall and increased frequency of flood events due to climate change have introduced increased uncertainty in the long-term performance of such infrastructure assets. Hence, efficient and reliable infrastructure inspection and monitoring are needed to ensure these systems run smoothly at a reasonable cost.
This PhD aims to develop a framework for digital twinning (DT) of railway bridges and provide informed decisions for their repair and maintenance schemes. DT can be imagined here as a digital representation of a physical asset (i.e., a railway bridge) which serves as a ‘living’ digital simulation model and is enabled by the abundance of data (e.g., operational data acquired from the bridge) and advanced data processing and interpretation routines.
The proposed aim will be achieved using the following objectives:
- Development of three dimensional as is geometry of a bridge using photogrammetry and deep learning for defect detection, e.g., cracks;
- Development of a visualisation suite of data from sensors based on building information modelling;
- Development of a physics-based approach for assessing the structural behaviour of masonry arch bridges using high fidelity models;
- Development of real time statistical model for sensor data analysis;
- Development of data centric engineering approach through the construction of a framework for digital twinning for bridges.
Please state your entry requirements plus any necessary or desired background: First or Upper Second Class UK Bachelor (Honours) or equivalent in a computer science, civil engineering or related background.
Subject Area: Civil & Structural Engineering, Computer Science & IT, AI & Machine Learning.
PhD Studentship: Bridge Management Through Digital Twin-Based Anomaly Detection Systems in Leeds employer: University of Leeds
The University of Leeds offers an exceptional environment for PhD students, providing a supportive and collaborative work culture that fosters innovation and research excellence. With generous funding packages, including a tax-free maintenance grant and additional top-ups for alumni, students are empowered to focus on their studies while benefiting from access to cutting-edge resources and expert supervision. Located in a vibrant city known for its rich academic heritage, the University encourages personal and professional growth, making it an ideal choice for those seeking meaningful and impactful research opportunities.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Bridge Management Through Digital Twin-Based Anomaly Detection Systems in Leeds
✨Tip Number 1
Network like a pro! Reach out to current PhD students or alumni from the University of Leeds. They can give you insider info about the programme and might even put in a good word for you.
✨Tip Number 2
Don’t just sit back and wait for opportunities to come to you. Attend relevant conferences, workshops, or seminars in civil engineering and digital twinning. It’s a great way to meet potential supervisors and show your enthusiasm!
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of digital twin technology and its applications in infrastructure. Be ready to discuss how your background in computer science or civil engineering aligns with the project goals.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for passionate candidates who are eager to contribute to innovative projects like this one.
We think you need these skills to ace PhD Studentship: Bridge Management Through Digital Twin-Based Anomaly Detection Systems in Leeds
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your application to highlight how your skills and experiences align with the PhD project. We want to see your passion for digital twinning and anomaly detection, so don’t hold back!
Showcase Relevant Experience:If you've worked on projects related to civil engineering, AI, or data analysis, be sure to mention them! We love seeing how your background fits into our vision for this studentship.
Be Clear and Concise:Keep your writing straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Remember, we want to understand your ideas without getting lost in complex language!
Apply Through Our Website:Don’t forget to submit your application through our official website. It’s the best way to ensure we receive all your materials and can review them properly. We’re excited to see what you bring to the table!
How to prepare for a job interview at University of Leeds
✨Know Your Stuff
Make sure you understand the key concepts of digital twinning and anomaly detection systems. Brush up on relevant technologies like photogrammetry and deep learning, as well as their applications in railway bridge management. This will show your interviewers that you're genuinely interested and knowledgeable about the field.
✨Showcase Your Skills
Prepare to discuss your technical skills and how they relate to the project. If you've worked on similar projects or have experience with data analysis, be ready to share specific examples. Highlight any relevant coursework or projects from your degree that align with the objectives of the PhD.
✨Ask Smart Questions
Come prepared with insightful questions for your supervisors. Inquire about their current research, the challenges they face in the project, or how they envision the future of digital twinning in infrastructure management. This demonstrates your enthusiasm and critical thinking.
✨Be Yourself
While it's important to be professional, don't forget to let your personality shine through. The interviewers want to see if you'll fit into their team. Be honest about your motivations for pursuing this PhD and what excites you about the research area.
