Predictive Maintenance AI: Anomaly Detection Intern

Damen Shipyards Group; Gorinchem; On-site; EUR 13,000 - 18,000; Full time

Benefits offered by this job

Academic mentoring

Travel allowance

High-impact project

Publication possibility

Multidisciplinary team exposure

Job summary

Damen Shipyards Group is offering an internship in the Data Science team (RD&I) in Gorinchem. You will work on an anomaly detection project for Smart Maintenance, leveraging sensor data to detect abnormal equipment behavior and support maintenance decisions.

You will focus on a research topic chosen with the team, with room to shape scope, potentially as a thesis, and start as soon as possible depending on your availability. The role includes mentoring and opportunities for publication.

Qualifications

• Pursuing a Bachelor's or Master's in a technical field.
• Background in data science or engineering is preferred.
• Experience with Python and statistics is desirable.
• English fluency required for collaboration.

Responsibilities

• Support the development of ML-based anomaly detection models for vessel equipment.
• Preprocess and analyze sensor/time-series data from onboard systems.
• Run experiments in Python and evaluate model performance.
• Collaborate with Data Scientists, maintenance engineers, and vessel operations.
• Document results and present findings to the team.

Skills

Python

Statistics

Time-series analysis

Machine learning

Fluent English

Education

Bachelor's/Master in Data Science / Applied Math / CS / Mechanical Eng

Tools

LSTMs

Job description

Damen Shipyards Group is offering an internship in the Data Science team (RD&I) in Gorinchem. You will work on an anomaly detection project for Smart Maintenance, leveraging sensor data to detect abnormal equipment behavior and support maintenance decisions.

You will focus on a research topic chosen with the team, with room to shape scope, potentially as a thesis, and start as soon as possible depending on your availability. The role includes mentoring and opportunities for publication.