Key Skills

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. Your qualities 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 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. 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. More information For more information about this position, please contact Dr Christa van IJzendoorn at [email protected]. Please do not send your application to this email address but use the ‘Apply Now’ button. Candidates for this vacancy will be recruited by Utrecht University. Apply now 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. external link If you have any questions about accessibility, for example regarding your workplace Vening Meineszgebouw A external link , the application process or your work, please contact us via our HR contact page external link 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. To apply, please send the following documents via the 'apply now' button: Your curriculum vitae (max 2 pages), including relevant research experience, publications and work carried out during your PhD and/or previous postdoctoral positions. Your motivation letter (max 1 page), in which you show your enthusiasm for the topic of this position, describe why this position fits your background and interests, and explain how your expertise would contribute to the project. Please also explain why you are interested in working at Utrecht University and within the Department of Physical Geography. The names and contact details of at least two references The first interview is online via MS Teams in the week of 23 November. If applicable, the second interview is in the week of 30 November. 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.

Core Responsibilities

Evaluate wind field predictions from AeoLiS against field measurements and/or CFD simulations, identify limitations in its assumptions, and train a machine-learning surrogate wind field model using CFD data. Integrate the surrogate with AeoLiS, validate the combined model on existing case studies, contribute to field data collection when needed, and communicate results to stakeholders and the scientific community.

Requirements

Applicants must have a PhD by the start date in data science, computer science, physics, Earth sciences, civil engineering, or a related field, along with strong numerical modelling and programming experience, preferably in Python. They should have a strong background in data science and experience applying machine learning or other AI techniques, communicate clearly in English, and be collaborative and curious; familiarity with AeoLiS, CFD, or aeolian field measurements is an advantage.

Benefits

• Holiday Pay
• Year-End Bonus
• Pension Scheme
• Partially Paid Parental Leave
• Flexible Employment Terms
• Personal and Professional Development Opportunities
• Flexible Leave Arrangements
• Additional Vacation Days