Physics‑Informed ML Internship: Time‑Series & Adaptive Control

Vacaturebank; Netherlands; Remote; EUR 8,900 - 13,000; Full time

Benefits offered by this job

Internship allowance

Job summary

Boskalis is seeking a university master's student to start a graduation project focused on enhancing dredging operation recommendations. You will explore physics-informed machine learning, time-series modelling, and adaptive control to improve existing models used for real-time operation settings on vessels.

You will collaborate with data scientists and engineers, conduct literature reviews, and develop methods that combine physical knowledge with data-driven approaches.

Qualifications

• You are pursuing a university master’s degree in computer science, AI, mathematics, physics, or a related field.
• Affinity with Python and version control.
• Able to develop, train, and evaluate ML models.
• Theoretical understanding of physics-informed ML, time-series modelling, or adaptive control; practical knowledge preferred.
• Proficiency in writing reports and presenting results.
• Preference for working at our Papendrecht office at least 3 days a week.

Responsibilities

• Collaborate with data scientists and engineers to understand the challenge.
• Conduct literature review on physics-informed ML, time-series modelling, and adaptive control and their application to the problem.
• Formulate the problem combining physical knowledge with data-driven models.
• Develop methods to improve the model's recommendations and test generalization across vessels and conditions.
• Document and report final findings with reproducibility of results.

Skills

Python

Version control

ML model development

Physics-informed ML

Education

Master's degree in computer science, AI, mathematics, physics

Tools

Databricks

Git

Job description

Boskalis is seeking a university master's student to start a graduation project focused on enhancing dredging operation recommendations. You will explore physics-informed machine learning, time-series modelling, and adaptive control to improve existing models used for real-time operation settings on vessels.

You will collaborate with data scientists and engineers, conduct literature reviews, and develop methods that combine physical knowledge with data-driven approaches.