Two Delft projects obtain funding within NWO – GROOT
In the programme NWO Open Competition Domain Science - GROOT, twenty new consortia will start a large research project. This boost of more than 47 million euros will make new research possible within consortia in which research groups create added value through collaboration. TU Delft is in the lead in two of the projects: HiRISE, aimed at charting the current state of Antarctica’s ice shelves with high resolution and accuracy, and CURE, which should lead to methods that enable sustainable management of contaminated waste management sites.
State and fate of Antarctica’s gatekeepers: a High Resolution approach for Ice ShElf instability (HiRISE)
Dr B. Wouters, TU Delft
Antarctica is the single largest unknown in the current projectons of sea level rise. For a large part, this is due to the uncertainty of how ice shelves will evolve in a changing climate. To reduce this uncertainty, we combine field measurements, satellite data and climate models to chart the current state of Antarctica’s ice shelves with high resolution and accuracy. This knowledge will then be exploited to improve our estimates of how the stability of the ice shelves will change in the coming centuries, in which way this impacts the ice loss of Antarctica and what this implies for water levels at the Dutch coast.
Coupled multi-process research for reducing landfill emissions (CURE)
Dr. Julia Gebert
Worldwide, landfilling waste is still an important aspect of waste management. Emissions of landfill gas (especially methane) to the atmosphere and dissolved contaminants to the groundwater are the result of bio-geochemical reactions in the waste package. In this project, in the context of a practical trial into the sustainable aftercare of landfill sites in the Netherlands, fundamental research is being conducted into the relationships between the conversions of organic matter in the waste and the emissions of pollutants. The research results will lead to methods that enable sustainable management of contaminated sites, with minimal emissions of contaminants to the environment.
Optimization for and with Machine Learning (OPTIMAL)
In this project, led by Tilburg University, Delft reseachers Karen Aardal en Leo van Iersel are involved
Machine learning has often made headlines in recent years, with spectacular applications such as image recognition and self-driving cars. A key component of this technology is mathematical optimization, that is used, for example, to train the underlying neural networks. The goal of this project is to provide new analysis and tools for optimization problems and algorithms arising in machine learning, but also to use insights and tools from machine learning to improve optimization methods. We will test our insights on classification problems in the medical sciences, decision problems related to the UN World Food Programme, and routing of shared, self-driving cars.