Swiss Federal Institute for Forest, Snow and Landscape Research WSL
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PostDoc in Machine Learning and Urban Water Scarcity Modelling Swiss Federal Institute for Forest, Snow and Landscape Research WSL in Switzerland
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Nov 15, 2026
Country
Switzerland
University
Swiss Federal Institute for Forest, Snow and Landscape Research WSL
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About this position
The Swiss Federal Institute for Forest, Snow and Landscape Research WSL is recruiting a postdoctoral researcher for the NOVA-City research program, which examines how medium-sized Swiss cities can build long-term resilience, environmental sustainability, and quality of life amid changing climatic, ecological, and societal conditions.
The postdoctoral project, WaterScarCity: Water Scarcity Forecasting for Swiss Cities, will develop approaches to help cities prepare for increasingly frequent and intense heat and drought. The research will use probabilistic subseasonal forecasts and connect models developed at WSL, ETH, EMPA, and EAWAG.
The researcher will work at WSL’s Research Unit Mountain Hydrology and Mass Movements, a multidisciplinary environment focused on novel operational hydrological forecasting systems. The project brings together machine learning, hydrometeorology, climate and weather modelling, spatial-temporal urban modelling, and applied data science. Methods include long short-term memory networks, graph neural networks, statistical forecasting, probabilistic prediction, and big-data processing.
The role also involves translating research into practical tools, potentially including user-friendly web-based applications developed in consultation with city stakeholders and other end users. Research outcomes are expected to contribute to scientific journal publications and to the development of resilient urban water-management approaches.
Funding details
Available
What's required
Applicants should be early-career scientists with a doctoral degree in environmental sciences or a related discipline, including data sciences. They should have excellent knowledge of machine-learning methods such as LSTM and graph neural networks; experience using weather and climate model output; strong programming skills in R and Python; expertise in statistical and probabilistic forecasting; experience in hydrometeorological and spatial-temporal urban modelling; in-depth knowledge of applied data science and big-data processing; and an above-average publication record appropriate to their career stage. Experience developing user-friendly web applications with end users and stakeholders is advantageous. Applicants should work independently, conceptually, and precisely, communicate well, cooperate in an international research team, and be fluent in English.
How to apply
Submit a complete application by 15 November 2026 through the online application portal. Upload a motivation letter, CV, doctoral certificate, and contact details for two references; applications by email will not be considered.
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Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.