Doctoral Position in Machine Learning Avalanche Forecasting

Overview

Organisation/Company ETH Zürich. Research Field:
Computer science » Other Environmental science » Earth science Physics » Computational physics. Researcher Profile:
First Stage Researcher (R1). Final date to receive applications: 20 Apr 2026 - 21:59 (UTC). Country:
Switzerland. Type of

Contract:

Temporary. Job Status:
Part-time. Is the job funded through the EU Research Framework Programme? Not funded by a EU programme. Is the Job related to staff position within a Research Infrastructure? No.

Doctoral Position in Machine Learning for Avalanche Forecasting

The Swiss Data Science Center (SDSC) and the WSL Institute for Snow and Avalanche Research (SLF)
are seeking a PhD student for a Swiss National Science Foundation (SNSF) project, starting May 1st.

This role sits at the intersection of applied machine learning, natural hazards, and snow/avalanche physics. The project is named Towards high-resolution, intelligent, spatiotemporal avalanche forecasting (THIRST in short).

The position will be based at the SDSC Zurich office (Andreasturm), with co-supervision from researchers at the WSL Institute for Snow and Avalanche Research (SLF) in Davos and the Chair of Alpine Mass Movements at ETH Zurich.

The host institution, the SDSC, is a national research infrastructure in data science and artificial intelligence (AI) of the ETH Domain, with EPFL and ETH Zurich as founding partners. Its mission is to enable data-driven science and innovation for societal impact, and it drives its initiatives in research projects, knowledge and technology transfer, and education. With a large multidisciplinary team of professionals in Lausanne, Zurich and Villigen, the SDSC provides expertise and services to various domains, such as health and biomedical sciences, energy and sustainability, climate and environment, and large-scale scientific infrastructures.

For more information please visit our website.

The project team includes a second PhD student (hosted at SLF Davos and the University of Zurich) and a dedicated software engineer (based at SLF Davos). The PhD will be supervised by Dr. Michele Volpi and Prof. Johan Gaume at ETH Zurich’s Department of Civil, Environmental, and Geomatic Engineering (d-BAUG).

Project background

Predicting snow avalanches—both in time and space—remains a major challenge, despite its critical role in saving lives and reducing infrastructure and mobility disruptions. This project aims to support human expert forecasters by developing machine learning models and a related infrastructure, capable of processing large, heterogeneous datasets with complex spatio-temporal dynamics and underlying physical processes.

While machine learning models now approach human-level performance on average, they still struggle with rare and critical conditions due to a lack of physical grounding. This PhD project aims to enhance traditional machine learning methods by integrating physics, empirical rules, and heterogeneous observations from different modalities, all aiming at improving the prediction quality and reliability of key avalanche formation and propagation parameters—critical for forecasters and downstream models.

The project will leverage a wealth of newly available data sources, including, but not limited to, avalanche observations from seismic sensing and real time detections, remote sensing data, physical and numerical models and simulations, empirical laws and past avalanche danger level bulletins.

The proposed system which the PhD student will help developing will incorporate a human-in-the-loop approach, ensuring expert forecasters retain full control—such as dynamically correcting errors and retraining models.

The PhD student is expected to carry out independent research and propose creative, grounded, and well thought solutions to a range of questions involving modelling, processing, forecasting and processing of key parameters representing snow, climate and avalanche processes, by relying on state-of-the-art machine learning models.

Specifically:

• Design, develop and implement baselines machine learning models.
• Extend, design, develop and implement machine learning models which…