PostDoc in Machine Learning and Urban Water Scarcity modelling 80 - 100% (f/m/d)
Core
Develop probabilistic subseasonal forecasts for urban water scarcity to help Swiss cities prepare for heat and drought using novel hydrological forecast systems.
Role type
PostDoc researcher (Machine Learning & Hydrology)
Builds
Probabilistic subseasonal forecasts for water scarcity
Domain
Environmental science, hydrology, urban water management
Deliverable
production ML models
Required skills
Machine Learning (LSTM, Graph Neural Networks), weather and climate model output analysis, programming (R, Python), statistical and probabilistic forecasting, hydrometeorological modelling, spatial temporal modelling, Big Data processing
Preferred skills
Web-based application development, stakeholder consultation
Technologies
LSTM, Graph Neural Networks, R, Python
Responsibilities
Interface various models (WSL, ETH, EMPA, EAWAG) with sub-seasonal forecasts; publish outcomes in scientific journals
Seniority
PostDoc (early career scientist)
