Working at Boskalis is about creating new horizons and sustainable solutions. In a world where population growth, an increase of global trade, demand for (new) energy and climate change are driving forces, we challenge you to make your mark in finding innovative and relevant solutions for complex infrastructural and marine projects.

Job Description

Are you looking for a practical internship where your work on reinforcement learning can contribute directly to real-life construction projects?

Many Boskalis projects involve reinforcing the dikes that protect the Netherlands against flooding and sea-level rise. These projects require large quantities of soil to be excavated, transported and reused. Planning these soil flows efficiently can reduce both project costs and emissions. We have developed an in-house soil-flow optimization tool for dike construction projects. Soil-flow planning is a complex problem involving different soil classes, quantities, locations and timing constraints. Engineers need to determine when and where excavated soils should be reused, stored, transported, sold or disposed of while meeting the project's construction requirements and optimizing objectives such as cost, transport, emissions and circular use of materials.

The tool combines a discrete-event simulation (DES) of the construction process with reinforcement learning (RL). The simulation represents the project environment, including supply and demand locations, timing, soil characteristics and logistics. Your challenge is to extend this existing model to a new dike construction project and train it to optimize new objectives, with a particular focus on KPIs like project cost or emissions. You will work with an existing model and codebase, real project data, and engineers to make the model perform on a real-world use case.

We also welcome a student who critically evaluates the existing modelling approach and identifies opportunities for improvement, for example through reward shaping, robustness metrics or hyperparameter tuning. If you demonstrate aptitude during this applied development assignment, your findings could provide the starting point for a follow-on academic thesis project, if you wish to continue the research.

Your main tasks will be:

Understand the existing DES and RL architecture and how project constraints are translated.
Help configure and extend the existing model for a new dike project and environment.
Design and implement reward functions for cost, emissions or other relevant project KPIs, while ensuring that fundamental objectives such as completing the dike on time with available materials remain satisfied.
Train and evaluate Maskable PPO agent(s) and analyze the effect of reward shaping, hyperparameters and different optimization objectives.
Test and validate model performance against defined project use cases and benchmark scenarios, together with engineers and data scientists.
Use metrics and experiment tracking to understand why an agent learns a particular behavior rather than only reporting its final reward.
Document experiments and recommendations so that results can be reproduced and used in further development.

Your qualities and experience

You are currently studying at university level in Artificial Intelligence, Computer Science, Operations Research, Applied Mathematics or a closely related field.
You have practical or academic experience with reinforcement learning.
You understand the fundamentals behind PPO and actor-critic methods, rather than treating an RL algorithm purely as a black box.
You understand concepts such as reward functions, discount factors, exploration, training stability and hyperparameters.
You are comfortable programming in Python.
Experience with MLflow and Databricks is an advantage.
You enjoy debugging complex systems and interpreting why an ML model behaves as it does.
You can work independently but are comfortable discussing assumptions and results with engineers and data scientists.
Most importantly, you are critical and curious: when an experiment produces an unexpected outcome, you want to understand why and propose what to try next.

You do not need to be an expert in dike construction or soil logistics. We are looking for someone with strong RL foundations and the motivation to learn the engineering domain.

The following competences are also important:

A good command of written and spoken English, currently living in the Netherlands. 
Strong communication and collaboration skills as well as the ability to work independently.
A mindset geared towards continuous learning and curiosity for tackling new challenges.
Ability to handle feedback and reset your course if needed.

If you are excited about the opportunity to work on innovative projects and have the drive to make a significant impact, we would like to hear from you. Explain to us why your background and expertise make you the ideal candidate for our AI program and join us in shaping the future of Boskalis. Apply now and help us shape the future of our industry.

What Boskalis offers

A flexible student position alongside your studies.
The opportunity to contribute to software that is used on real tenders and projects.
Supervision from experienced software developers, simulation engineers and AI specialists.
Hands-on experience with Python software development in a professional environment.
Access to well-established development facilities and compute power
Exposure to dike and offshore construction, logistics optimization, and simulation technology.
A collaborative and innovative working environment.
Potential opportunities for internships, graduation projects, or future employment within Boskalis.

Qualifications

What you can expect

A real-world RL challenge : Work on an existing RL application being deployed for real Boskalis dike construction projects rather than a standalone university exercise.
Ownership : Take responsibility for experiments from formulation and implementation through training, evaluation and recommendations.
Technical guidance : Work alongside ML engineers and learn from an experienced RL specialist.
Engineering exposure : Collaborate with project engineers and learn how data, physical constraints and operational objectives are translated into an optimization problem.
Flexible commitment : The internship can be structured for 2–5 days per week, depending on availability.
Timing : We are looking for someone who can start as soon as possible (January).
Potential continuation : Strong results may lead to an opportunity to continue the work, potentially as a full-time academic thesis, built around a research question emerging from your experiments.
Location : The team is based in Papendrecht, and regular office presence is preferred so you can work directly with the developers and engineering stakeholders.

Additional Information

Additional information

We’ll be happy to answer your questions about the internship of Reinforcement Learning. Please contact Tim Gouweleeuw, Corporate Recruiter via tim.gouweleeuw@boskalis.com.

Interested?
Apply by completing your details and uploading your CV and motivation letter via our career’s website.

Please note: Boskalis never requests a financial contribution to arrange visas or other documents related to a job vacancy. Be wary of individuals who pretend to be our recruiters and do ask for such contributions.

Disclaimer for Recruiters and Recruitment Agencies
We appreciate your interest in our vacancies and understand that your candidate might be enthusiastic about this exciting opportunity. However, our recruitment process is not structured this way, at Boskalis we handle recruitment ourselves. Therefore, we do not accept unsolicited applications or CVs from recruitment agencies. Any submission will be treated as a direct application.

Practical Internship: Reinforcement Learning in Logistics

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