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  3. PhD Position Hydraulic Structures (Piping Erosion)

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PhD Position Hydraulic Structures (Piping Erosion)

Most of the flood prone cities around the world relay on natural and man-made flood defences to protect themselves against coastal and riverine flood events. …

ongeveer 2 maanden geleden

Arbeidsvoorwaarden

Standplaats:
Mekelweg, Delft, Zuid-Holland
Dienstverband:
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
32 - 38 uur
Salarisindicatie:
€ 2395 - € 3061 per maand
Opleidingsniveau:
WO

Functieomschrijving

Most of the flood prone cities around the world relay on natural and man-made flood defences to protect themselves against coastal and riverine flood events. While many of the deterioration mechanisms which may compromise the stability of these structures are well understood, backward piping erosion is still one of the most difficult failure mechanisms to model and predict making it one the biggest threats for flood defence systems. The two main reasons for this are that a) flood defence structures such as dikes are often funded over highly heterogeneous soil systems which are difficult to represent and b) the available computational models for correctly representing the process and its inherited heterogeneities are still highly computationally demanding. Significant numerical advances, systems safety assessment studies and case databases are already available regarding backward piping erosion in dikes but there is still a big knowledge gap in how to combine and harmonize these data sets and models to produce more robust methods for its deterministic and probabilistic assessment. In the actual state of the art of machine learning, methods like semi-supervised learning, deep learning and reinforces learning open a window of opportunities to produce more robust models when combining multiple sources of information.

For these reasons, the PhD intended research aims to improve the modelling and probabilistic prediction of piping erosion by combining detailed physically based models with other sources of information with the help of machine learning techniques so that piping erosion modelling accuracy and detail are improved while making them computationally feasible for probabilistic assessment of dike safety against backward piping erosion. It is expected that the actual state of the art for better representing this failure mechanism will significantly improve while making it more accessible for the actual hydraulic and geotechnical engineering by developing open-source codes and robust design rules.

The project will be developed as part of the research lines of the Hydraulic Structures and Flood Risk section in the Hydraulic Engineering department at TU Delft, under the supervision of Dr. Juan Pablo Aguilar-López and Prof. Dr. Bas Jonkman. Throughout the project, you will be given the opportunity to work with leading academic and industrial partners, both in the Netherlands and abroad.

Functie-eisen

We are looking for an excellent candidate with the following qualifications, knowledge and skills:

  • A M.Sc. degree in the areas of Hydraulic Engineering, Civil Engineering, Environmental engineering, Geotechnical Engineering or other related areas.
  • Knowledge in hydraulic engineering, hydraulic structures, geotechnics and groundwater modeling.
  • Coding skills in on of the progaming lenguages such as Python, Matlab or Fortran.
  • Modeling experience in finite element packages such as COMSOL, OpenFoam or Abaqus is a plus.
  • Either experience of high motivation to learn different machine learing applications is also a plus.
  • Independent, self-motivated, curiosity-driven and open to communicate and collaborate with peers.
  • Excellent knowledge of English, both verbally and in wring. During the PhD trajectory English is very important. So if your mother language is not English and you do not hold a degree from an institution in which English is the language of instruction, you must submit proof of English proficiency from either TOEFL (minimum total score of 100) or IELTS (minimum total score of 7.0). Proof of English language proficiency certificates older than two years are not accepted.

Conditions

TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3061 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

Additional information

For more information about this vacancy, please contact J.P. (Juan) Aguilar-López, Assistant Professor in flood defence resilience, email: j.p.aguilarlopez@tudelft.nl.