17 beste vacatures voor de functie van machine learning in 3028 rotterdam delfshaven (nieuwe medewerkers gezocht)

Graduation Internship: Offshore Weather Simulations
Boskalis —Papendrecht
- Schrijfvaardigheden
- Master
- Analytisch vermogen
8 uur
Intelligent Automation Architect
Deloitte —Rotterdam
- Parttime
- Hybride werken
- Azure
- Bachelor
- AI
- Winstdeling
- Zorgverzekering
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Senior Full-Stack Data Engineer
Eneco —Rotterdam
- € 88.000 - € 131.000 per jaar
- Hybride werken
- Azure
- DevOps
- SQL
- Winstdeling
- Werk vanuit huis
2 uur
Senior Compiler Engineer
Innatera Nanosystems —Rijswijk
- Fulltime
- C++
- Computervaardigheden
- Communicatieve vaardigheden
Snel solliciteren
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Data Engineer
Verpact —Leidschenveen
- € 55.000 - € 65.000 per jaar
- Online sollicitatie
- Hybride werken
- Azure
- DevOps
- SQL
- Reiskostenvergoeding
Data scientist
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Technolution —Gouda
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- Fulltime
- Engineering
- Java
- Master
- Winstdeling
VP of Quantum Software Engineering
Equal1 —Delft
- Fulltime
- Doctor
- Informatica
- Engineering
Snel solliciteren
Senior SDK Hardware Simulator Engineer
Innatera Nanosystems —Rijswijk
- Fulltime
- Hybride werken
- Informatica
- Master
- C++
Snel solliciteren
Lead Engineer
Zeppelin Power Systems Netherlands B.V. —Papendrecht
- € 3.500 - € 5.000 per maand
- Online sollicitatie
- Hybride werken
- AutoCAD
- Mechanische ervaring
- Microsoft Office
- Reiskostenvergoeding
- Zorgverzekering
Snel solliciteren
Mechanisch Engineer
WPS —De Lier
- Microsoft Office
- Communicatieve vaardigheden
Senior .NET Developer (AI first)
Scan Sys —Nootdorp
- Hybride werken
- Azure
- DevOps
- AI
- Reiskostenvergoeding
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Good Company —Den Haag
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Xuntos —Den Haag
- E-mail
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CGI —Rotterdam
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- Azure
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Technical Product Manager
JINGDONG RETAIL (UK) LIMITED —Rotterdam
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10 uur
.NET Developer - AI First
PANGAEA Digital Agency —Den Haag
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- Fulltime
- Hybride werken
- DevOps
- ERP systemen
- SQL
- Reiskostenvergoeding
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9 uur

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Graduation Internship: Offshore Weather Simulations

Boskalis -
Papendrecht

Nu solliciteren

Vacaturegegevens

Stage
8 uur geleden

Kwalificaties

Toegepaste wiskunde
Schrijfvaardigheden
NumPy
Wiskunde
Master
Analytisch vermogen
SciPy
AI
Python

Volledige vacaturetekst

How you can make your mark

At Boskalis, logistics simulations are increasingly used to support tenders and projects in offshore energy and dredging. These simulations help engineers evaluate work methods, fleet configurations and the impact of weather downtime on project performance. Weather conditions and offshore workability for vessels are key drivers of uncertainty and project risk.

The current workability assessment approach relies on hindcast analysis, using more than 40 years of hourly metocean data to statistically evaluate weather downtime and operational windows. While this provides robust insight into long-term weather uncertainty, it generally considers a predefined operational strategy and does not explicitly capture how vessel crews adapt to changing conditions.

Capturing such adaptive behavior requires modelling decision points and branching pathways within a Discrete Event Simulation (DES). In addition, DES models are increasingly used not only to evaluate project performance under uncertainty but also to optimize operational strategies, vessel deployment, and work sequences. Both objectives require many simulation runs: adaptation introduces multiple possible project pathways for each weather scenario, while optimization requires repeated evaluation of alternative decision configurations and operational plans.

As a result, model complexity and computational requirements increase substantially. Rather than evaluating a single project trajectory for each weather scenario, the simulation must assess numerous possible pathways across thousands of weather realizations derived from the hindcast dataset, often repeatedly as part of an optimization procedure. This computational burden can become a limiting factor when applying DES to large-scale weather workability analyses.

To address the computational burden associated with weather-adaptive DES models, various model reduction and approximation techniques can be explored. Potential approaches include clustering weather conditions into representative weather states, modelling weather evolution using Markov-chain-based methods, and developing surrogate models that emulate the behavior of computationally intensive simulations.

The challenge lies in striking an appropriate balance between computational efficiency and the fidelity required to accurately represent weather-driven offshore operations.

The goal of this research is therefore twofold:

To investigate which modelling techniques can significantly accelerate probabilistic DES for weather workability applications.
To determine whether the additional modelling detail gained from explicitly representing operational decision-making under weather uncertainty actually leads to more accurate and valuable project insights compared to current workability analyses.

You will work within the AI Department and collaborate closely with simulation engineers, R&D engineers, metocean engineers, and offshore energy planners to develop, evaluate, and validate next-generation weather workability simulations for Boskalis projects.

During this project you will:

Perform a literature review on various model reduction and approximation techniques, uncertainty quantification and workability assessments.
Analyze existing Boskalis weather workability and logistical simulation models.
Identify operational decisions and weather-driven branching mechanisms suitable for modelling in DES.
Design and implement the selected technique approaches in Python.
Compare accuracy, robustness and computational performance across different modeling approaches.
Develop demos for practical engineering use cases.
Present findings to engineers, data scientists and business stakeholders.

Your qualities

You are a Master’s student in Applied Mathematics, Computational Engineering, Operations Research, Marine Engineering, Transport and Logistics, or related quantitative discipline

You have:

Strong Python programming skills
Affinity with simulation, optimization, data analysis and uncertainty modeling
Knowledge of Gaussian Processes, Bayesian methods, surrogate modelling, or machine learning
Experience with computing libraries such as SimPy, Numpy and SciPy
Interest in offshore operations and maritime engineering
Strong analytical and problem-solving skills
Good communication and report-writing skills

We offer

A challenging MSc thesis project with direct impact on real-world engineering projects.
Supervision from AI engineers, simulation experts and domain specialists.
Access to industrial-scale simulation models and datasets.
Opportunity to work on cutting-edge applications of AI in maritime engineering.
A collaborative environment combining data science, optimization and operational decision support.
Possibility to continue the work through an internship or future employment opportunities.

What you can expect

A dynamic environment: A job where you can make an impact, as part of a diverse, international team of experts.
Career development: We have a professional career development track for AI roles. Develop yourself with (online) courses at our Boskalis academy, both on personal and professional AI development.
The Boskalis Campus: Experience the unique Boskalis vibe at our Papendrecht site, complete with restaurants, sports fields, and a wharf where our vessels dock. We offer a state-of-the-art auditorium, brainstorming rooms, experience center for client meetings, and a barista corner where you can connect with colleagues.
Young Boskalis: Are you under 36? Come and join Young Boskalis! Have fun and join in social and sports activities ranging from pub quizzes to yoga, bootcamps and an annual sailing boat race. Networking and knowledge sharing are a vital part of Young Boskalis as well.

About Boskalis

Working at Boskalis is about creating new horizons and sustainable solutions. In a world where population growth, an increase of global trade, demand for offshore 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.

Interested?

We are more than happy to answer your questions about the position of Student Vacancy. Please contact Tim Gouweleeuw, Recruiter via [email protected].

Interested? Please apply by filling in your details and by uploading your cover letter and CV on our careers site.

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machine learning vacatures in 3028 rotterdam delfshaven

Graduation Internship: Offshore Weather Simulations

Intelligent Automation Architect

Senior Full-Stack Data Engineer

Senior Compiler Engineer

Data Engineer

Data scientist

Software Engineer Java / Rust

VP of Quantum Software Engineering

Senior SDK Hardware Simulator Engineer

Lead Engineer

Mechanisch Engineer

Senior .NET Developer (AI first)

Data Engineer

AI Data Architect

Senior AI Architect - Telecommunicatie

Technical Product Manager

.NET Developer - AI First

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Ik wil de nieuwste vacature-alerts ontvangen voor de functie van machine learning in 3028 rotterdam delfshaven

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