Natural hazards such as earthquakes, floods, and landslides pose significant risks to human populations, infrastructure, and ecosystems. These hazards rarely occur in isolation; they often trigger or interact with other processes, leading to multi-hazard effects. For instance, an earthquake may trigger landslides, which in turn can block rivers, causing floods. Accurate prediction and simulation of such multi-hazard events is essential for improving disaster preparedness, risk mitigation, and resilience. However, modeling these interconnected physical processes is extremely complex. Traditional numerical methods like finite element or finite difference models are highly effective in simulating individual hazards, but they face limitations when applied to multi-hazard scenarios, especially at large scales or in real time due to data scarcity and undefined/uncoupled physical models. The interplay between different physical processes involves nonlinearity, high-dimensional parameter spaces, and uncertainty, all of which make conventional methods impractical for multi-hazard systems. Physics-Informed Neural Networks (PINNs) offer a promising new approach. By embedding physical laws directly into neural networks, PINNs can simulate complex systems while ensuring that the predictions remain consistent with the governing equations of the underlying physics. This hybrid approach has shown great potential for reducing computational costs and improving the accuracy of models, even in data-scarce environments.
This PhD position is funded by the Dutch national earth and environmental sector plan, to advance the scientific field of AI on early warning systems. It aims to develop advanced Physics-Informed Neural Networks (PINNs) for modeling the complex interactions between multiple geophysical processes, specifically earthquakes, and landslides. The primary objective is to create a unified, scalable framework that accurately simulates these coupled hazards while addressing computational efficiency and uncertainty quantification. Central to this work is the coupling of partial differential equations (PDEs) governing seismic wave propagation, soil stability, and potentially multi-phase flow, allowing the PINNs to capture the nonlinear, multi-scale interactions between these hazards. This research will also investigate feedback mechanisms and energy transfer between hazards, embedding these principles within the network architecture to simulate how an initial hazard evolves into a multi-hazard event. By optimizing PINN architectures, this research aims to enable real-time, large-scale multi-hazard simulations, offering a powerful tool for early warning systems in data sparse regions.
Information and application
For more information about the position, you can contact Ashok Dahal (email: a.dahal@utwente.nl). You are also invited to visit our homepage.
For questions about working and living in the Netherlands please consult the official website of the Netherlands Government or the website of the Expat Centre East Netherlands.
Please submit your application before December 15th, 2024. Potential candidates are requested not to use LLM tools (e.g. ChatGPT) to write their application. Your application should include:
- A cover letter with your favorite programming language (and why) to prove that you have read the full vacancy text, applications without such explanation will not be considered.
- Curriculum Vitae with a description of relevant academic/professional achievements, and at least two references with their contact details.
- An academic transcript of BSc and MSc education, including grades.
- Abstract and first two pages of your last academic result (MSc thesis/final project report/EngD thesis) to serve as an academic writing sample.
- A statement of research for this position, where you should explicitly mention scientific gaps that you would like to focus your research on.
The interviews will be held after the second week of January.
About the department
The Department of Applied Earth Sciences combines earth scientific knowledge with dynamic modelling and advanced remote sensing, to analyze earth systems and processes in space and time. Our goal is to contribute to global challenges concerning future demands for earth resources and to help reduce disaster risk and the impact of natural hazards on communities living in changing environments.
About the organisation
The Faculty of Geo-Information Science and Earth Observation (ITC) provides international postgraduate education, research and project services in the field of geo-information science and earth observation. Our mission is capacity development, where we apply, share and facilitate the effective use of geo-information and earth observation knowledge and tools for tackling global wicked problems. Our purpose is to enable our many partners around the world to track and trace the impact – and the shifting causes and frontiers – of today’s global challenges. Our vision is of a world in which researchers, educators, and students collaborate across disciplinary and geographic divides with governmental and non-governmental organisations, institutes, businesses, and local populations to surmount today’s complex global challenges and to contribute to sustainable, fair, and digital societies.
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