Postdoc: Machine learning for wind flow prediction in coastal dunes

Sport Society; Utrecht; Hybrid; EUR 41,000 - 64,000; Full time

Benefits offered by this job

Fixed-term contract

Competitive salary

8% holiday pay

Pension scheme

Flexible working

Growth opportunities

Job summary

Utrecht University invites applications for a 2-year postdoc focused on machine learning for wind flow prediction in coastal dunes. You will build a surrogate wind field model by fusing AeoLiS outputs with CFD data and train ML models in Python to deliver fast, data-driven predictions for coastal dune scenarios.

The project aims to enhance coastal dune models and support decision-making, with results disseminated through stakeholder meetings, conferences, and journals.

Qualifications

• PhD required in a relevant field.
• Solid background in data science and ML.
• Experience with numerical modelling and Python is preferred.

Responsibilities

• Evaluate wind field predictions from AeoLiS vs field/CFD data.
• Train ML models (e.g., PySR or neural networks) on CFD data.
• Develop surrogate wind field model and integrate with AeoLiS.

Skills

Machine learning

Python

Data science

Numerical modelling

Education

PhD in Data Sciences/CS/Physics/Earth Sciences/Civil Engineering or related

Tools

AeoLiS

CFD

PySR

Job description

Postdoc: Machine learning for wind flow prediction in coastal dunes

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.

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 incoastal dune management.

The ultimate goal of this project is tobuild 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.

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.

Our offer

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 collectivelabouragreement, we offer attractiveadditionalbenefits, including opportunities for personal and professional growth , flexibleleavearrangements,and extravacationdays. 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 .

About us

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.

Candidates for this vacancy will be recruited by Utrecht University.

At Utrecht University, we strive to be a place where everyone feels at home. We value colleagues with different backgrounds, perspectives and identities, including differences in culture, religion or ethnicity, gender, sexual orientation, neurodiversity, disability and age. We are committed to creating a safe and inclusive environment where everyone can thrive and contribute. Read more about our commitment to diversity and inclusion.

Knowledge security screening can be part of the selection procedures of academic staff. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology.

If you have an international (non-Dutch) Master’s diploma, you will be requested to provide your Bachelor’s and Master’s diplomas as well as the corresponding grade lists (in English) if you are selected for this position.

The preferred start date is 1 January 2027, but this is flexible. Regardless of the start date, the contract needs to end at or before 31 December 2028. This means that if the contract starts later than 1 January 2027, it will be shorter than 2 years; if it starts earlier, it may be longer than 2 years (depending on available funding).

Note that international candidates that need a visa/work permit for the Netherlands require at least four months processing time after selection and acceptance. This will be arranged with help of the International Service Desk (ISD) of our university. Finding appropriate housing in or near Utrecht is your own responsibility, but the ISD may be able to advise you therewith. Unfortunately, we must warn that it is a tight market at the moment. In case of general questions about working and living in The Netherlands, please consult the Dutch Mobility Portal .

The application deadline is 6 November 2026.