PhD in Digital Twins for Flood-Resilient Infrastructure

Delft University of Technology; Delft; On-site; EUR 42,000 - 60,000; Full time

Job summary

Delft University of Technology in Delft, Netherlands, invites applications for a research-focused position to develop Digital Twin modules for flood risk analysis.

You will combine physics-based and machine learning approaches, validate results on infrastructure datasets, and implement solutions in end-user platforms.

We value MSc in related field, strong modelling and programming skills in Python/SQL/Java, and experience engaging with external partners.

Qualifications

• MSc degree required in Civil Engineering, Computer Science or Hydro-informatics.
• Strong modelling, data management and ML knowledge.
• Proven track record with scientific writing.

Responsibilities

• Develop functional modules for Digital Twins such as risk analysis and scenario modelling.
• Develop hybrid physics-based / machine learning modelling for flood risk.
• Integrate functionalities into end-user platforms.
• Co-organise workshops and coordinate stakeholder engagement.
• Prepare reports and disseminate findings to partners.

Skills

Numerical modelling

Hydrodynamic simulation

Data management

Machine learning

Scientific reporting

Communication with stakeholders

Education

MSc degree in Civil Engineering, Computer Science or Hydro-informatics

Tools

Python

SQL

Java

PyTorch

Job description

Develop functional modules for Digital Twins, such as risk analysis, scenario modelling and processing of monitoring data. Develop hybrid (physics based/machine learning) modelling for flood risk. Apply and validate the Digital Twin functionalities for simplified representation of infrastructure (first) and real case studies (second). Implement the developed functionalities into end users' platforms. Co-organise workshops and co-coordinate stakeholder engagement (e.g. regular meetings, presentations, visits).

A MSc degree in Civil Engineering, Computer Science or Hydro-informatics (or in a related discipline).

Demonstrated experience in numerical modelling, hydrodynamic simulation and data management. Scientific and/or practical knowledge of machine learning. Excellent programming skills (e.g. in Python, SQL, Java, PyTorch, etc.). A critical, creative and pro-active approach to research, with distinctive problem-solving skills. Proven experience with writing scientific reports/publications. Confidence/willingness in engaging with partners and stakeholders. Excellent command of English (oral and written).

CV (max 4 pages). Cover letter (max 1 page) describing your motivation and qualifications for the position. Applicants selected for an interview will be asked to provide contact information of at least 2 references.