Postdoc: Machine learning for wind flow prediction in coastal dunes

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

Benefits offered by this job

Holiday pay

Year-end bonus

Pension scheme

Job summary

Universiteit Utrecht is inviting applications for a 2-year postdoc position in coastal dune modelling. You will use numerical modelling and machine learning to increase the accuracy of coastal dune models and to build a surrogate wind field model feeding robust predictions into existing dune simulations.

You will start by evaluating wind field predictions from AeoLiS, train data-driven predictors from CFD data, and apply the new model to case studies.

Qualifications

• PhD in Data Sciences, Computer Sciences, Physics, Earth Sciences, Civil Engineering, or related field.
• Strong data science background with ML experience.
• Experience applying AI techniques to physical systems.
• Strong programming skills (Python preferred).
• Affinity with collecting and analysing field measurements is a plus.

Responsibilities

• Evaluate wind field predictions from coastal dune models against field data or CFD results.
• Train a data-driven wind field predictor using CFD data (e.g., PySR or neural networks).
• Develop a surrogate wind field model and integrate it with the AeoLiS coastal dune model.
• Apply the new model to existing case studies and validate results with stakeholders.

Skills

Data science

Machine learning

Python

Numerical modelling

Field measurements

Education

PhD in Data Sciences

Tools

AeoLiS

CFD

Job description

Coastal dunes are dynamic landforms that provide flood protection and crucial habitat in the Netherlands and across the world. Coastal dunes are formed by sand transport, which strongly relies on the interaction between wind and dune shape. Recent improvements in airflow modelling and coastal dune modelling are promising. However, getting accurate wind field predictions remains challenging, especially in dunes with complex topography.

Your job

In this 2-year postdoc position, you will use numerical modelling and machine learning techniques to increase the accuracy of coastal dune models. As a result, your project will inform and enhance decision-making in coastal dune management.

The ultimate goal of this project is to build a surrogate wind field model that can feed accurate wind field predictions into numerical coastal dune models.

You will start by evaluating current wind field predictions from the coastal dune model AeoLiS by comparing model output with existing field measurements and/or Computational Fluid Dynamics (CFD) simulations. Specifically, you will assess where simplified assumptions in the model fall short. You will then train a machine learning model (such as PySR or neural networks) on CFD data to develop a fast, data-driven wind field predictor.

You will combine this surrogate model with AeoLiS and evaluate the accuracy of the new model setup by applying it to existing case studies. Where needed, you will contribute to field data collection to support model validation. You will share your results in stakeholder meetings, scientific conferences, and academic journals.

Requirements

• You have a strong background in data science and experience applying machine learning and other AI techniques. You are interested in applying these techniques to physical systems, in this case, to coastal dunes, wind flow dynamics and wind-driven sand transport.
• You have an open, collaborative and curious attitude. You enjoy exploring new approaches and you combine this with the pragmatism that is needed to move a project forward.
• By the time the position starts, you have obtained a PhD degree in Data Sciences, Computer Sciences, Physics, Earth Sciences, Civil Engineering, or a related field.
• You have a strong background in numerical modelling and programming (preferably Python). You will collaborate with colleagues who are AeoLiS and CFD specialists, so affinity with these techniques is a plus but not required.
• Affinity with the collection and analysis of (aeolian) field measurements is a plus.
• You communicate clearly and have a strong command of the English language.

Conditions of employment

We offer:

• a position (1.0 FTE) for 24 months.
• a working week of 36 - 40 hours and a gross monthly salary between €3.706 and €5.760 (salary scale 10 under the Collective Labour Agreement for Dutch Universities (CAO NU)). The salary is based on a 38-hour working week;
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.
• In addition to the terms of employment set out in our collective labour agreement, we offer attractive additional benefits, including opportunities for personal and professional growth, flexible leave arrangements, and extra vacation days. Through the UU Terms of Employment Options Model, you can tailor your employment package to your needs. In this way, we encourage you to grow in what you do, both in your work and in your development. Read more about our terms of employment.

Employer

Universiteit Utrecht

A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University, the various disciplines collaborate intensively towards major strategic themes. Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Pathways to Sustainability.

Sharing science, shaping tomorrow.

Utrecht University’s Faculty of Geosciences studies the Earth: from the Earth’s core to its surface, including man’s spatial and material utilisation of the Earth – always with a focus on sustainability and innovation. With 3,400 students (BSc and MSc) and 720 staff, the faculty is a strong and challenging organisation. The Faculty of Geosciences is organised in four Departments: Earth Sciences, Human Geography & Spatial Planning, Physical Geography, and Sustainable Development.