Internship – Machine Learning in Flood Modelling

Arcadis is the global partner at the forefront of the most impactful projects of our time. We help our clients make sustainable choices through the combination of digital innovation, expertise, and future-oriented skills in areas such as environment, energy, water, buildings, transport, and infrastructure. We take that extra step to offer our clients tailored solutions for design, engineering, and consultancy. By utilizing data-driven insights, we shape the natural and built environment together.

With more than 34,000 people, we bundle global expertise and tackle challenges such as climate, affordable energy, and livable cities together. We improve quality of life through our presence in more than thirty countries.

We are looking for a motivated graduate student for the period from October 2026 through February 2027 with a strong interest in the application of machine learning within flood modelling. During this MSc thesis internship, you will develop, train, and test machine learning models that support 2D flood simulations. The goal is to reduce the computation time of simulations while maintaining reliability and physical interpretability. Your activities combine data science, numerical modelling, and issues surrounding climate adaptation.

Climate change increases the risk of large-scale flooding, both through extreme precipitation events (pluvial flooding) and through extremely high river discharges (fluvial flooding), which can lead to dike breaches. Flood simulations are essential tools to map the consequences of such scenarios and to support the development of flood risk maps.

Traditional flood simulations use physics-based numerical models that solve hydrodynamic equations. Although these models are accurate, they can be very computationally intensive, especially for large areas or high-resolution models. Machine learning offers promising possibilities to accelerate these simulations, but there are still challenges regarding the reliability, generalizability, and physical interpretability of the models. This thesis research focuses specifically on these challenges.

The main goal of this project is the design, development, and evaluation of a machine learning model for 2D flood simulations. The exact content can be tailored to your interests, but is expected to be as follows:

1. You will first develop and test a proof-of-concept machine learning model based on fictitious or synthetic training data to predict inundation levels after a dike breach.
2. Subsequently, you will apply the developed methods to a practical case using existing flood simulation data for the area of the Rhine and the IJssel.

Throughout the project, you will critically assess the performance, robustness, and applicability of the model within the professional practice of flood modelling.

• You will work on current climate adaptation issues with direct societal relevance.
• You combine machine learning, hydrodynamic modelling, and data analysis within a single project.
• You will gain experience with state-of-the-art modelling techniques applied within professional engineering practice.
• You will receive intensive guidance and technical support from specialists from our River, Coast & Sea team.
• Team and work environment
• You will be part of the River, Coast & Sea advisory group, a multidisciplinary team working on projects in the fields of river science, flood risk management, climate adaptation, and numerical modelling, both in the Netherlands and internationally.
• The thesis research will be carried out from our office in Amersfoort.

• You are pursuing an MSc in Civil Engineering, Hydraulic Engineering, Earth Sciences, Applied Mathematics, Physics, Data Science, or a similar field.
• You have an affinity with hydrodynamic (numerical) modelling and flood risk analyses.
• You have experience with programming in Python or are highly motivated to develop this further.
• You have an interest in machine learning, artificial intelligence, and data science.
• You are motivated to work on issues surrounding climate adaptation and water safety.
• You possess strong analytical skills and can communicate results clearly.
• You are available full-time for your thesis research during the internship period.
• You are present at our office in Amersfoort at least three days per week.

We achieve our goals by enabling everyone to get the best out of themselves. We believe that everyone's contribution matters. That is why we pioneer with an approach based on your skills and build a 'Skills Powered Organisation'. With your unique experience and expertise, you will map out your own career path. Together we maximize impact! Your work makes the difference. Whatever role you play, you help provide sustainable solutions for a healthier planet. Leave your mark on your career, your colleagues, clients, your life, and the world around you. On our website, you can read the blogs of Arcadians to find out how we do this.

Arcadis offers you a position with a lot of freedom to independently shape your work in an ambitious and driven team. In addition to a great work culture, we have also included excellent employment conditions, including:

• 30 days of leave with full-time employment, with the possibility to buy or sell extra days.
• Employees may use the NS business card for private use free of charge.
• Additional arrangements regarding partner parental leave and paid parental leave.
• Possibility for hybrid working, facilitated by an appropriate home office allowance and a professionally equipped workspace at home.
• Good pension scheme with the Dutch Pension Fund (HNPF), where Arcadis pays the largest part of the premium.
• Opportunities to follow courses and training, access to our extensive Arcadis Learning Experience Platform (LXP).

Join Arcadis. Create a Legacy.

Our commitment to equality, diversity, inclusion, and belonging:

We want you to be your best self every day. We take equality and inclusion very seriously and we hold ourselves accountable for our actions. We want to be an employer of choice and offer everyone a great workplace.

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An assessment and/or pre-employment screening may be part of the application procedure.