Postdoc: ML for Wind Flow in Coastal Dunes

Utrecht University; Netherlands; On-site; EUR 41,000 - 64,000; Full time

Benefits offered by this job

Pension scheme

Holiday pay

Year-end bonus

Job summary

Utrecht University seeks a data-driven postdoctoral researcher to advance coastal dune modelling. You will combine numerical wind-field models with ML on CFD data to create fast, surrogate wind predictors that enhance AeoLiS-based simulations.

Requirements include a PhD in a relevant field and strong Python skills. The role includes stakeholder outreach, conference presentations, and journal publications in a collaborative environment.

Qualifications

• In-depth data science and ML experience applied to physical systems.
• Strong numerical modelling background and programming (Python).
• Experience with wind-field or coastal dune modelling is a plus.
• Clear communication; proficiency in English.

Responsibilities

• Develop surrogate wind-field models to feed coastal dune models.
• Evaluate AeoLiS wind field predictions against CFD/ field data.
• Train ML models on CFD data for fast predictions.
• Contribute to field data collection and disseminate results in meetings and journals.

Skills

Data science

Machine learning

Python

Education

PhD in Data Sciences

PhD in Computer Sciences

PhD in Physics

PhD in Earth Sciences

PhD in Civil Engineering

Tools

PySR

Neural networks

CFD simulations

AeoLiS

Job description

Utrecht University seeks a data-driven postdoctoral researcher to advance coastal dune modelling. You will combine numerical wind-field models with ML on CFD data to create fast, surrogate wind predictors that enhance AeoLiS-based simulations.

Requirements include a PhD in a relevant field and strong Python skills. The role includes stakeholder outreach, conference presentations, and journal publications in a collaborative environment.