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What are you going to do?
As a datascientist, you will develop and maintain data pipelines, using our wide variety of data sources. These originate from our model scale basin experiments, on-board measurements, simulator studies and output from time domain simulations and CFD computations. In your projects you are bridging the gap between the research outcome and the requested data driven applications by other MARIN departments. To achieve this, collaborating with colleagues and other internal stakeholders is key.
Other tasks are to develop methods to ensure the quality of our data sources and the outcome of predictions during its lifecycle. Furthermore you will be involved in the development of MARIN DS/AI packages coded in Python. Together with the team you will take care of internal software package releases for the MARIN DS/AI applications and advice internally about the deployment and proper use of these packages.
What do we offer?
MARIN is a globally recognized top institute in maritime research, based in Wageningen, the Netherlands. Our mission is ‘Better Ships, Blue Oceans’: we stand for clean, smart and safe shipping and sustainable use of the sea. We do this as an independent knowledge partner for the maritime sector, governments and society. We work with around 400 specialists in various fields who are proud of an open and collaborative culture in which you can explore your ideas and take initiative.
You will join our growing Data Science team, which is part of the Research & Development department. The total R&D department consists of around 50 researchers and software engineers. As data science team we conduct research, develop and maintain data driven methods for maritime operations.
Data science and Artificial Intelligence are identified as key technologies in the MARIN strategy for the future. The data science programme is part of the R&D technology plan and consists of four sub-programmes: bringing cutting-edge machine learning methods to the maritime domain; data-driven autonomous decision making or decision support, e.g. reinforcement learning; combining first-principles methods with data-driven methods; addressing robustness and explainability of data-driven methods.
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