The Internet of Things (IoT) is a concept in which a system of connected devices, or things, are able to communicate to each other without the necessity of human involvement. While this infrastructure is currently mainly used for collecting data and using these data as is, one can also imagine a world in which the combination of multiple devices can be used for the creation of so-called digital twins: digital representations of real world objects (e.g., buildings). With these twins, one would essentially ask targeted questions to the twin regarding the state of its physical counterpart, aggregated from the different sensors, instead of inspecting the raw sensor values. This project will investigate the use of digital twins, algorithms to create digital twins, and working on applications that deal with sensor data.
eWEning star is a “fresh from the oven” Start-Up, which is currently developing a discovery tool that serves stakeholders in the renewable energy sector with relevant scientific information regarding renewable energy. Currently people in this sector use key-word based search queries in order to find scientific papers and reports, but with eWEning star’s concept, these papers are smartly categorized, saving users a lot of time and nerves. By making the search process more efficient we can make the energy transition towards renewables faster! Currently we have around 900 documents that are manually categorized in three different ways: (i) perspective, (ii) position in value chain, and (iii) geographical location. Combined, we have created 15 categories. Depending on the length of your internship, it is possible to work on these all, or choose one out of the three options. While this manual approach is feasible for a small number of papers, it does not scale well. Our aim is to apply Machine Learning to improve this process. We expect that machine learning can provide us with a fast solution for categorizing already published papers according to eWEning star concept. You are given the freedom to design, develop and test a process which leads to the automated categorization. You have a background in Data Science and/or computer science, and you have natural curiosity for solving issues. You aren’t afraid to ask questions if you seem to “hit the wall”, but are capable of working independently. Some entrepreneurial mentality is a benefit as eWEning star is a Start-Up. Good communication skills are needed towards non-technical founder.
The current water infrastructure in the northern part of the Netherlands generates a large amount of data. In this project, the student is asked to work on one (or more) data science projects related to water management. This includes, but is not limited to: leakage detection, usage prediction, anomaly detection, missing data reparation, GIS analysis, and many more. Findings from this research might find their way back into the production systems of the water management company we work with.