Physics-Informed ML Research Internship

Boskalis; Papendrecht; On-site; EUR 4,500 - 8,900; Full time

Benefits offered by this job

Internship allowance

Office Papendrecht 3 days a week

Young Boskalis

Job summary

Boskalis is offering a graduation/internship project in Papendrecht for a master student to advance ML-based vessel operation settings. You will explore physics-informed ML, time-series modelling, and adaptive control, collaborating with data scientists and engineers to craft a data-driven, physically grounded approach.

You will review literature, formulate problems, and develop methods to improve model recommendations with reproducibility across vessels and conditions, reporting your findings

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.

Responsibilities

• Literature review across physics-informed ML, time-series modelling and adaptive control.
• Determine methods to combine physical knowledge with data-driven models.
• Formulate the problem and validate improvements using historical data.
• Develop methods to improve model recommendations and demonstrate generalization across vessels and conditions.
• Report final findings with reproducible results.

Skills

Python

ML modeling

Data science

Report writing

Physics-informed ML

Education

Master's degree in CS/AI/Math/Physics

Tools

Databricks

Git

Python tooling

Job description

Boskalis is offering a graduation/internship project in Papendrecht for a master student to advance ML-based vessel operation settings. You will explore physics-informed ML, time-series modelling, and adaptive control, collaborating with data scientists and engineers to craft a data-driven, physically grounded approach.

You will review literature, formulate problems, and develop methods to improve model recommendations with reproducibility across vessels and conditions, reporting your findings