Data Science Intern — Predictive Maintenance for Ships

Damen Shipyards Group; Gorinchem; On-site; Full time

Job description

Overview

Damen's Research, Development & Innovation (RD&I) department develops and implements the technology and know-how to support its ambition of becoming a sustainable and digitally connected shipyard. You will join the Data Science team within Damen RD&I in Gorinchem, where the focus is on applying data and AI solutions to shipbuilding and maritime operations. The team includes experts in physics-informed machine learning, simulation acceleration, predictive maintenance, computer vision, and operational analytics.

This internship is part of Smart Maintenance, a strategic project aimed at using AI to detect abnormal equipment behavior on board vessels before it leads to failure or unplanned downtime.

The role

As an intern, you will work on the Smart Maintenance project, where an anomaly detection algorithm has been developed to help engineers spot early signs of equipment problems, such as engines, pumps, propulsion, and cooling systems, before they escalate into failures. Vessels generate large amounts of sensor data during operation, and the goal is to turn that data into reliable, trustworthy signals that support maintenance decisions.

You will contribute to an existing pipeline that learns what "healthy" equipment behavior looks like and flags deviations from it. Your primary focus will be on a dedicated research topic, selected together with the team, that strengthens a specific part of this pipeline — from data selection to detection reliability, health trending, explainability, or deployment. There is room to shape the exact topic based on your interests and background, either before or shortly after you start. This can be a thesis or graduate internship and could start from September onwards.

Possible research topics

• Model transferability across vessels: exploring how an anomaly detection model trained on one vessel can be adapted to other vessels, machinery types, or operating environments — including retraining, recalibration, and drift detection strategies.


• Reliable anomaly detection: improving detection models to minimize false alarms, adapt to different operating conditions, handle transient events, and quantify prediction confidence.


• Health and degradation trending: moving beyond fault detection to identify gradual performance degradation, developing health indicators that give early warning of wear or efficiency loss.


• Explainable AI and fault diagnosis: making anomaly models explainable, identifying which sensors or components drive an alert, and supporting root-cause analysis for engineers.


• Defining "healthy" operation: identifying, selecting, and validating representative data from vessels operating under normal conditions, accounting for varying operating modes and environmental influences.

Key accountabilities

• Support the development and improvement of ML-based anomaly detection models for vessel equipment.


• Preprocess and analyze sensor/time-series data from onboard systems.


• Run experiments in Python, evaluating model performance against real and/or simulated data.


• Work closely with Data Scientists, maintenance engineers, and vessel operations stakeholders.


• Document results and present findings to the team regularly.

Skills & Experience

We are looking for a student who:

• Is currently pursuing a Bachelor or Master in Data Science, Applied Mathematics, Computer Science, Mechanical/Electrical Engineering, or a related technical field.


• Ideally combines data science with a mechanical/electrical engineering background, with the ability to model equipment behavior and understand which sensors are informative for which failure modes.


• Has experience with Statistics, Python, and ideally with machine learning (LSTMs) or time-series analysis.


• Has an interest in predictive maintenance, sensor data, or industrial/marine systems.


• Is comfortable working with real-world, sometimes messy, operational data.


• Communicates fluently in English.

What we offer

• Mentoring at academic level throughout the internship.


• Internship/graduation fee and travel allowance for the duration of the assignment.


• Opportunity to contribute to a high-impact predictive maintenance project used in real vessel operations.


• Research publication is likely possible with a possible extension of the internship period.


• Exposure to a multidisciplinary team combining data science and maritime engineering expertise.