Data Science Internship with CERC/SLF

Data Science Internships with externe SeiteCERC/externe SeiteSLF

Accurate flood forecasting is essential to ensure that timely action can be taken to prevent loss of life and minimize damage to infrastructure and property due to flooding. Flood events in rivers are usually caused by a combination of weather events (e.g. storms) and previous moisture, i.e. previously saturated soils that can reduce infiltration capacity and lead to increased runoff rates. After drought events, pre-wetness is low and large rain events may also be less likely to cause flooding. However, flooding is also possible in these cases.

Project: You will investigate whether floods preceded by drought are more difficult to predict on short and medium time scales than floods that occur independently of drought events. This project aims to set up an LSTM for a subsample of Swiss river catchments, using sub-daily discharge data to predict historical flood events that occur both immediately after a drought and independently of a drought. You would build the model structure, train the model using the available data and evaluate the performance for both cases.The overall aim is to determine whether there is a difference in predictive skill between independent and successive flood events, which, if so, could have important implications for hydrological risk.

The project will take place in an inspiring environment at the SLF and offsite with online supervision.

Qualifications required: Bachelor¡¯s, Master¡¯s students at ETH Zurich; familiarity with machine learning, strong programming skills (preferably R or Python), experience in working with large datasets, ideally with climate and hydrological data

Start date: October 2024

Duration: 6 to 8 weeks. It is expected that you spend two weeks of that time in Davos.

Financials: Train travel to/from and accommodation in Davos are covered. A CHF 800 stipend from ETH Zurich will be provided.

More information and application: Please send your complete application including a short cover letter, CV, certificate & transcript of your highest degree earned compiled in one PDF to Bailey Anderson,