PhD Researcher in Granular Physics and Machine Learning for Submarine Landslide Modeling

4 weken geleden

Enschede, Overijssel, Nederland University of Twente Voltijd

We are seeking a highly motivated PhD researcher to join our team at the University of Twente, working on a novel high-performance multi-phase material point method for modeling submarine landslides.

This project is part of the EU-funded Marie Curie Doctoral Network POSEIDON, which aims to develop solutions to improve the resilience of offshore infrastructures. The overarching objective is to develop a novel volume-coupled formulation to consider the transport and coupling of multiple phases.

The doctoral candidate will implement constitutive models for saturated sediments in fluid- and solid-like states into an existing GPU-MPM code. The exchange of momentum, mass, and energy between these admissible states of saturated sediments will be achieved with overlapping subdomains where the transition can potentially take place.

To further improve the computational efficiency, machine learning surrogates will be used to partially replace the expensive physics-based models to allow large-scale industrial applications. The project aims to provide more accurate, highly efficient, and physics-based predictions for submarine landslides in order to quantitatively assess the risk of damages to offshore infrastructures and induced disasters.

Objectives:

i) Extend an existing MPM code from single-phase to multi-phase based on a volume-coupled formulation that incorporates mass, momentum, and energy exchange between slow- and fast-flowing sediments and water;

ii) Develop a reduced-order model of the multi-phase system using machine learning and integrate it into the volume-coupled numerical framework;

iii) Perform simulations of submarine landslides and assess their impact on offshore infrastructures;

iv) Calibrate model parameters using existing experimental data and compare the model predictions with experimental data obtained from laboratory flume experiments.

Expected Results:

i) A novel volume-coupled formulation to consider the transport and coupling of multiple phases;

ii) An open-source, multi-phase GPU-based MPM code with clear documentation, tutorials, and examples for future users;

iii) Benchmark cases validated using experimental data from laboratory-scale physical tests.

Requirements:

We are looking for a highly motivated and talented individual with a strong background in granular physics, computer modeling, and machine learning techniques. The ideal candidate should have a Master's degree in a relevant field and experience with numerical methods for solving partial differential equations. Previous experience with multi-phase materials and transport phenomena and/or deep neural network solvers will be advantageous.

The University of Twente offers a dynamic ecosystem with enthusiastic colleagues in which internationalization is an important part of the strategic agenda. We offer a full-time 4-year PhD-position, with excellent mentorship and a stimulating research environment. Collaborations with a dedicated, dynamic research team and external parties from several European countries involved in the POSEIDON project are also available.

What We Offer:

We offer a competitive salary, a minimum of 232 leave hours in case of full-time employment, and a full-time employment in practice means 40 hours a week, resulting in 96 extra leave hours on an annual basis. We also offer excellent fringe benefits, including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, a solid pension scheme, free access to sports facilities, and a family-friendly institution that offers parental leave (both paid and unpaid).

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