Postdoc position in the simulation of coastal plastic pollution

In this postdoctoral position, you will contribute to a major research project to improve our understanding of macroplastic pollution in coastal seas. Working at the intersection of numerical modelling and observations, you will develop simulations that support both scientific insight and practical applications.

Plastic items larger than a few centimeters (macroplastics) are a very visible testament of the pollution in the ocean and have an impact on marine organisms, and even more so when they fragment into microplastics. However, the amount of macroplastic can vary by orders of magnitude on scales of kilometers and days. In this large project funded by an NWO Vici grant, our team will develop a new numerical tool to understand this variation and predict the amount of macroplastic on Northwest European seas and coasts. Such a tool could be used to optimise beach clean-ups and thereby prevent these macroplastics from fragmenting into large numbers of microplastics.

During the project, you will be involved in:

• setting up hydrodynamic coastal flow fields using SWAN, SWASH, SCHISM or a comparable model;
• writing python code to advect virtual macroplastic items in these flow fields using the Parcels-code.org framework;
• exploring the dispersion of these macroplastic items in these flow fields;
• comparing the results of the simulations to results from an experimental campaign of floating trackers;
• collaborating with a postdoc and two PhD candidates who simulate dispersion of macroplastic in an ocean general circulation model of the Northwest European shelf;
• further developing and improving the Parcels-code.org code;
• working within a large, interdisciplinary project.

At the end of the project, you will have:

• a deep understanding of the hydrodynamic processes that control the dispersion of buoyant macroplastic items in the coastal zone;
• expertise in developing for open-source software projects such as Parcels-code.org;
• had the opportunity to set your own research questions and goals;
• participated in fieldwork to deploy and recover the floating trackers;
• further advanced your skills in numerical modelling and data analysis in python;
• enhanced your skills to communicate your scientific results to a wide audience including peers, media, and the general public.