Supervisors

Dr Jawad Fayaz, Department of Computer Science, University of Exeter

Prof Edward Keedwell, Department of Computer Science, University of Exeter

Location: Department of Computer Science, Streatham Campus, Exeter.

Location:

Department of Computer Science, Streatham Campus, Exeter,

The Department of Computer Science at the University of Exeter is currently accepting applications for a fully funded PhD studentship, with a negotiable enrolment date open until January 2026 or earlier. For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £18,622 for 4 years full-time, or pro rata for part-time study.

Project Description

The IMPACT-RISE project is a pioneering initiative that seeks to revolutionize the field of community resiliency and sustainability analysis through a machine learning (ML) and explainable artificial intelligence (XAI) outlook. The project marks a significant advancement in improving public safety against both low-probability high-impact events and high-probability events with long-term impacts. It focuses on the development of state-of-the-art infrastructural surrogate models using physics-informed and interpretable ML techniques. Our aim is to comprehensively analyse and mitigate the risks posed by diverse extreme events, both natural and anthropogenic (including earthquakes, floods, storms, climate change), on built environment. The primary goal is to enhance our understanding and predictive capabilities, thereby improving decision-making processes to effectively reduce the impact of these hazards on infrastructure systems.

Central to IMPACT-RISE project is the development of data-driven deep learning (DL) based surrogate models that simulate the complex behaviours of infrastructure systems under conditions posed by various hazards (occurring independently and concurrently). These models will be trained while appropriately infusing physics (such as structural dynamics), ensuring not only high accuracy but also enhanced interpretability – a crucial factor for decision-makers in risk management and emergency response. To further boost the interpretability of the DL based surrogate models, principles of explainable artificial intelligence (XAI) will be integrated for a deeper understanding of the models' decision-making processes. Working on the project involves the meticulous collection, development, and analysis of diverse infrastructural and hazard related data sets, ranging from historical incident records to real-time infrastructural sensor data, community maps, and more. Furthermore, the project requires augmentation of real recorded data with simulation data obtained through structural finite-element modelling and analyses.

IMPACT-RISE project aims to provide accurate, reliable, and accessible models, thereby playing a pivotal role in fortifying community resilience and sustainability against various hazards. These innovative tools will be instrumental in pinpointing vulnerabilities, optimizing resource distribution, and crafting effective emergency response plans. IMPACT-RISE is grounded in collaborative effort, bringing together a diverse team of specialists in machine learning, civil engineering, and risk analysis. We are committed to align our models with the practical realities and unique challenges of different communities. Through this integrated and cooperative approach, IMPACT-RISE is set to establish new standards in community protection and infrastructure resilience, confronting the diverse challenges of the 21st century with advanced technological solutions and strategic insights.

The project is open-ended and offers flexibility, inviting applicants to suggest their unique ideas that align with the overarching theme and objectives of the initiative.

For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £18,622 for 4 years full-time, or pro rata for part-time study.
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

Your main supervisor will be Dr Jawad Fayaz (https://jfayaz.github.io/). If you have any specific questions regarding this studentship, please contact Dr Fayaz at j.fayaz@exeter.ac.uk

Key Research Aims

• Development and validation of accurate and efficient physics-informed ML/DL surrogate models of infrastructural systems.

• Simulation of infrastructural behaviour through advanced hazard modelling and finite-element modelling and analysis.

• Extensive data analysis of infrastructural responses to various extreme events for community resiliency and sustainability assessment.

• Integration of multidisciplinary approaches for enhanced predictability and reliability of trained DL models.

• Utilize principles of XAI to develop interpretability tools for the trained DL models.
Responsibilities:

• Conduct cutting-edge research in physics-informed ML/DL, Bayesian statistics, and XAI.

• Obtain and innovatively implement knowledge in advanced structural analysis, hazard analysis, and finite element infrastructural modelling.

• Collaborate with internal/external interdisciplinary teams, including engineers, data scientists, and risk analysts (as required).

• Publish findings in high-impact journals and present at international conferences.

• Engage in departmental activities and contribute to broader research goals.

Entry requirements

• Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second-Class UK Honours degree, or the equivalent qualifications gained outside the UK, in Computer Science, Civil/Structural Engineering, or a closely related field.

• Strong background in applied mathematics, computational methods, statistical modelling, or basics of machine learning.

• Familiar with the concepts of structural analysis, structural dynamics, hazard modelling, and finite-element modelling,  (the student will be expected to attain strong knowledge in the topics during the course of study).

• Proficiency in high-level programming languages such as Python, MATLAB, or similar.

• Excellent analytical, organizational, and communication skills.

• Previous research experience or publications in relevant fields would be advantageous.

• If English is not your first language you will need to meet the required level as per our guidance at https://www.exeter.ac.uk/pg-research/apply/english/

How to apply

Apply now

The application process will require you to upload following documents five-to-six documents.

1. Statement of purpose/cover letter explaining your background, interest in the project, and how your skills and experience match the research aims (One side of A4-page).

2. CV including academic achievements, publications, and relevant work experience (One side of A4-page).

3. Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)

4. Research proposal detailing your proposed approach to the project, including clear objectives, data collection and analysis techniques, and technical methodology (One side of A4-page).

5. Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted for the interview.

6. If you are not a national of a majority English-speaking country, you will need to submit evidence of your proficiency in English.

The application process will remain open until the position is successfully filled, Therefore, please apply at your earliest convenience.  We will be conducting interviews on a continuous basis, inviting shortlisted candidates for virtual interviews via MS Teams or Zoom, as applications are reviewed.

If you have any general enquiries about the application process please email PGRApplicants@exeter.ac.uk or phone 0300 555 60 60 (UK callers) +44 (0) 1392 723044 (EU/International callers)  Project-specific queries should be directed to the main supervisor.

Summary