For MARIN Academy we are looking for a student for the following MSc internship/assignment (6-9 months):

The Mystery of the Green Passenger Machine Learning and Physics-Based Models for Quantitative Analysis and Reduction of Biofouling Impact on Maritime Transport Efficiency

Project background
For millennia, maritime navigation has been a cornerstone of human civilization. Oceans continue to serve as vital arteries for global trade, necessitating the construction, development, and maintenance of modern vessels. An issue that significantly impacts ship efficiency is biofouling - the accumulation of marine organisms on a vessel's hull when submerged in water over a period. The initial colonization of microbes gives rise to a slime layer, eventually facilitating the attachment of larger organisms such as barnacles, mussels, and seaweeds. The resulting biofilm presents a significant increase in viscous drag on the vessel, in extreme scenarios culminating in a resistance augmentation of up to 50%. Despite the magnitude of biofouling's impact, quantitative assessment of the phenomenon remains a challenge. Establishing an effective model for biofouling is thus of paramount importance to the maritime industry, promising insights into maintenance schedules, operational profiles, and more. Comprehensive understanding and prediction of this process could yield substantial reductions in both economic expenditure and environmental detriment. Traditional physics-based models offer the capability to forecast biofouling growth and its influence on ship resistance. However, the advent of machine learning has provided a fresh lens to view this issue. These data-driven models have entered the biofouling domain recently and have already demonstrated promising results, indicating their potential in advancing our understanding and management of marine biofouling.

Project goal
This project aims to develop a physics-based and a machine-learning-based model to study the growth and added resistance of biofouling on ships. Because both approaches require much investigation and different skillsets, a dual master’s graduation is proposed.

Project tasks
1. Conduct a comprehensive literature review on biofouling, emphasizing growth modeling and recent advancements in data-driven methodologies for assessment.
2. Develop a machine-learning model to quantitatively describe and predict the growth of biofouling on ship hulls and its subsequent impact on ship resistance, leveraging available experimental data sourced from scholarly literature and potential third-party contributions
3. Construct a physics-based model that can depict the biofouling growth process and its effects on ship resistance, allowing for comparisons and insights.
4. Validate both the machine learning and physics-based models utilizing available data.
5. Investigate the impact of biofouling on ship emissions and efficiency. Synthesize insights derived from the models to propose evidence-based recommendations for improving maritime practices.
6. Present a comprehensive review of the findings to the Maritime Research Institute Netherlands (MARIN), including recommendations and further research avenues.
7. Compile the findings, insights, and recommendations into a comprehensive thesis or journal article to report on the results of the graduation project."

If you are interested, Harm Jan Kamphof, Researcher can tell you more about the internship: h.j.kamphof@marin.nl.  
You can apply for this internship by using the APPLY button.