Internship or Graduation: Smart Maintenance

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About us

Damen aims to become the world's most sustainable and digitally connected shipyard. The Research, Development & Innovation department develops and implements the technology and know-how to achieve these ambitions. We actively assist the business in creating an innovative product portfolio and provide forward-thinking guidance to improve the quality and performance of Damen's products and services.

We fully understand that the key to our success lies in finding the right person for the job, but we also strongly believe that it does not stop there. That is why we will help you develop your skills and competences to guarantee you are successful in your job. You will work in a very diverse and multicultural group of professionals with a wide variety of backgrounds, but a strong shared interest in technology and a commitment to continuous learning.

The Research, Development & Innovation department, Mechanics & Systems Team, is offering a variety of Internship/Thesis assignment topics on the field of Data Driven Maintenance.

The Role

Driven by the ambition to become the most sustainable and connected shipbuilder in the world, Damen conducts Research on Sustainable Solutions & operations, Digitalization & Operational Excellence. One such field of research is Data Driven (‘Smart’) Maintenance. Data Driven Maintenance is an emerging field that promises to reduce downtime, costs and environmental footprint of the shipping industry. This all being facilitated by the rapid progress of Digitalization, Data acquisition & Cloud/Edge Computing in the maritime industry and boosted by developments in Data Science/AI-ML.

There are quite a few challenges in the transition towards Smart Maintenance. Possible approaches include physical/domain knowledge based methodologies, as well as novel methods based on data. Grey- box modelling (combination of physics and AI/ML methods) is also proposed as a future direction that combines benefits of both approaches. A ship is a complex system, made up of many sub-systems the health of which is important for safety, efficiency and operability. The failures of which can be identified in many possible ways (vibration/sound/electrical measurements etcetera).

Within this research field we can offer Thesis/Internship assignments on the fields of:

• Algorithm Development & Validation for Condition Monitoring Smart Maintenance (Physics/Machine Learning or combination)

• Derive Operational Insights from Vessel Data (System Health/Energy Efficiency/Downtime or Abnormalities). Mostly using AI/ML algorithms such as classifiers, anomaly detection methodologies, timeseries analysis.

• Fundamental research on machine learning/physics methodology development (e.g grey-box modelling, physics- informed ML, AI driven data pre-processing & quality evaluation, ML model hyperparameter optimization)

• Students are also free to propose topics themselves that align with both their interest & DAMEN vision

Key accountabilities

• Organize and plan your work in order to answer the research question

• Navigate a multidisciplinary field, proactively approach stakeholders for information or guidance, ranging from Data science, domain knowledge, measurement specialists

• Combine scientific and practical mindset and overview of the possible approaches to the problem with the goal of creating robust & validated methodologies following the state-of-the-art

Skills & Experience

• Affinity for working with data/algorithms & understanding of physical principles

• BSc/MSc in Engineering, Physics, Informatics, Data Science

• Knowledge of Python/Matlab

• Collaborate with colleagues of varying backgrounds

• Independent, curious, analytical, and proactive.

• Fluent in the English language and/or Dutch

What we offer

• An internship/graduation fee and travel allowance will be paid for the duration of the assignment.

• A pleasant and dynamic, international work environment with informal atmosphere

• Mentoring at academic level will be available throughout the internship.

Recruiter:

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