MSc thesis assignment: Automated Calibration of a Shoreline Model Using Satellite-Derived Shoreline Observations

Haskoning

Delft

On-site

EUR 8,100 - 9,900

Full time

14 days+

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Benefits offered by this job

Internship allowance €750/month

Mentor guidance from a dedicated tutor

Opportunity to contribute to impactful

Inclusive work culture

Job summary

Haskoning is offering a MSc graduation internship to develop an automated calibration workflow for the ShorelineS model using CoastSat shoreline observations. You will combine satellite data, shoreline modelling, and machine learning within an engineering-oriented workflow.

The role focuses on parameter calibration, uncertainty quantification, and applying methods to environments with structures and nourishments.

Qualifications

Master’s student in a relevant field.
Programming experience, preferably Python.
Interest in data-driven methods including ML.
Understanding coastal processes or willingness to learn quickly.

Responsibilities

Set up a ShorelineS model using nearshore wave conditions and initial coastline geometry.
Design and evaluate an automated calibration framework using satellite-derived shoreline observations.
Evaluate parameter uncertainty, model skill and implications for complex coastal areas.

Skills

Analytical thinking

Independent research

Geospatial analysis

Python programming

Machine learning

Education

Master’s student in Civil/Coastal Engineering, Physical Geography, Geospatial Sciences

Tools

Python

CoastSat

ShorelineS

Job description

Help develop a data-informed method for predicting shoreline evolution and informing nourishment design and coastal management decisions. As a graduation intern at Haskoning, you will combine satellite observations, shoreline modelling and machine learning in a workflow with direct engineering relevance.

600 - 7500
40 hours

MSc thesis assignment: Automated Calibration of a Shoreline Model Using Satellite-Derived Shoreline Observations

What you will do as a MSc graduation intern

Coastal engineers increasingly use satellite-derived shoreline observations to analyse shoreline behaviour over large spatial and temporal scales. CoastSat is an open-source toolkit that extracts shoreline positions from satellite imagery and has become widely used for coastal monitoring and research.

ShorelineS is a physics-based coastline evolution model developed by Deltares that simulates shoreline change under varying wave and sediment transport conditions.

This thesis investigates how satellite-derived shoreline observations from CoastSat can be used to automatically calibrate ShorelineS. The objective is to develop a probabilistic, data-informed calibration workflow inspired by approaches such as CoSMoS-COAST, while extending the methodology to more complex environments with structures, nourishments, and other forms of human interference.

Focus area of thesis:

Set up a ShorelineS model using nearshore wave conditions and initial coastline geometry.
Design and evaluate an automated calibration framework for ShorelineS using satellite-derived shoreline observations.
Evaluate parameter uncertainty, model skill and the implications for complex coastal areas with human interference such as structures or nourishments.

The goal of the research we are envisioning is to:

Use satellite-derived shoreline observations within an automated calibration framework to improve the predictive skill and uncertainty quantification of ShorelineS.

Depending on the students’ interests and proposed methodology, the research could include:

Preparing CoastSat shoreline observations for model calibration.
Identification and selection of influential ShorelineS calibration parameters.
Development of a calibration objective function relating modelled and observed shoreline positions.
Investigation of probabilistic, machine-learning, surrogate-modelling, or hybrid calibration approaches.
Assessment of uncertainty and implications for coastal engineering applications such as nourishment design and shoreline management.

The intended deliverable is a scientific thesis and a documented workflow that Haskoning can further develop for coastal engineering projects.

Where you will work

At Haskoning, you will join an independent, employee-owned international consultancy that combines engineering, design, and consultancy services with software and technology. Our mission, "Enhancing Society Together," drives us to create a positive impact on the world.

As our new graduation intern, you will become part of the team Coastal & River, Dynamics & Design, which falls under the Advisory Group Maritime and Renewables. Our team combines deep expertise in coastal and river processes with practical engineering design to deliver robust solutions for flood risk management, coastal protection, constructing marinas, ports, and nature‑based solutions for e.g. shoreline stability. In addition to modelling and design, the team develops and maintains advanced tooling. For example, Python‑based workflows and reusable analytical frameworks which support efficient and standardized analyses across our company. You will work alongside colleagues who combine coastal and river engineering knowledge with remote sensing and geospatial analytics. It is an ideal environment for bridging science and engineering!

Our team primarily works from the Delft and Amersfoort offices. Both are easily accessible by public transport. We therefore expect graduation interns to regularly work from one of these locations to maximize interaction with colleagues and supervisors. The more connected and engaged you are, the more rewarding your assignment will be!

What you bring

You are curious, analytical, and independent, with the drive to carry out a scientifically rigorous thesis that makes a real‑world impact. You enjoy taking ownership of an innovative topic, coding data analyses, exploring complex geospatial datasets, and translating scientific insights into practical, usable tools.

Requirements:

You are a Master’s student in a relevant field (e.g., Civil/Coastal Engineering, Physical Geography, Geospatial Sciences).
You have programming experience, preferably in Python, and are willing to work with an existing modelling codebase.
You understand coastal processes and numerical modelling, or are motivated to develop this understanding quickly.
You have an interest in data-driven methods including machine learning applications.
You are interested in coastal morphology, numerical modelling and satellite-derived shoreline observations
You can manage an independent research cycle, critically assess results and communicate findings in clear English.
And finally: Being enthusiastic about satellite data and levering this to leave a positive sustainable impact on the world!

What you can expect from us

During your internship at Haskoning, you’ll have the opportunity to grow professionally in an inspiring and inclusive work environment. We offer support, guidance, and attractive conditions to help you get the most out of your internship: