Doctoral Position in Machine Learning for Avalanche Forecasting 100%, Zurich, fixed-term The Swiss Data Science Center (SDSC) is 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 officially registered under 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, heterogeneous observations and 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. Job description 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 include knowledge about physics (e.g., physics constrained neural networks, scientific machine learning). Design, develop and implement multivariate time series forecasting models. Collaborate on dedicated and existing codebases with other project members and collaborators, and potentially members of the open source community. Collaborate with experts from different backgrounds (machine learning, physics, natural hazards, software engineering). Effectively present and communicate scientific materials, but also be able to engage with the broader public in the context of the project and beyond. Prepare scientific publications, present at seminar and workshops and engage with the institution teams. Profile Required qualifications and skills: Master degree in computer sciences, natural sciences, or equivalent with demonstrated expertise or qualifications (e.g., physics, geography, environmental sciences, environmental engineering, natural hazards). Knowledge and ability to quickly grasp novel physical concepts, spanning from theoretical formulations to numerical simulations. Ability to formulate and solve problems mathematically and transform that into usable scientific code. Experience applied machine learning research and solving real world problems. Solid and proven ability in scientific programming and prototyping in python using PyTorch and various machine learning libraries. Demonstrable commitment to open research, reproducibility and good standards in general. Desired qualifications and skills: Experience handling large and complex datasets. Knowledge of other programming / scripting languages used in the community (C, C++, R, Matlab, Octave, Julia, etc.) is beneficial. Knowledge or willingness to learn geographical information system and related technologies (databases). Experience in implementing, training and evaluating machine and deep learning models for spatio-temporal data. Positive attitude towards interdisciplinary collaboration, and willingness to frequently interact with the other team members and domain scientists. Interest in snow, avalanches, mountains, natural hazards is a plus. Workplace We offer A stimulating, collaborative, diverse and cross-disciplinary environment spread across several institutions (the position is hosted at ETH Zurich, but SDSC has offices at the EPFL and the Paul Scherrer Institut). We value work-life balance. Possibility to publish your research in top-ranked conferences and journals. Possibility to travel and present your work at different national and international events. Help supervise students and participate in lectures. chevron_right Working, teaching and research at ETH Zurich We value diversity and sustainability In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future. Curious? So are we. We look forward to receiving your online application with the following documents: Letter of Motivation Curriculum Vitae Electronic copy of your academic diplomas, grades and certificates Contact details from at least 2 but no more than 3 references Further information about Swiss Data Science Center can be found on our Website. Questions regarding the position should be directed to Dr. Michele Volpi, michele.volpi@sdsc.ethz.ch (no applications). Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence. About ETH Zürich ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow. Wenn Sie sich auf diese Stelle bewerben, erwähnen Sie bitte dass Sie sie auf Universitoxy gesehen haben.

Doctoral Position in Machine Learning for Avalanche Forecasting

100%, Zurich, fixed-term

The Swiss Data Science Center (SDSC) is 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 officially registered under 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, heterogeneous observations and 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.

Job description

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 include knowledge about physics (e.g., physics constrained neural networks, scientific machine learning).
Design, develop and implement multivariate time series forecasting models.
Collaborate on dedicated and existing codebases with other project members and collaborators, and potentially members of the open source community.
Collaborate with experts from different backgrounds (machine learning, physics, natural hazards, software engineering).
Effectively present and communicate scientific materials, but also be able to engage with the broader public in the context of the project and beyond.
Prepare scientific publications, present at seminar and workshops and engage with the institution teams.

Profile

Required qualifications and skills:

Master degree in computer sciences, natural sciences, or equivalent with demonstrated expertise or qualifications (e.g., physics, geography, environmental sciences, environmental engineering, natural hazards).
Knowledge and ability to quickly grasp novel physical concepts, spanning from theoretical formulations to numerical simulations.
Ability to formulate and solve problems mathematically and transform that into usable scientific code.
Experience applied machine learning research and solving real world problems.
Solid and proven ability in scientific programming and prototyping in python using PyTorch and various machine learning libraries.
Demonstrable commitment to open research, reproducibility and good standards in general.

Desired qualifications and skills:

Experience handling large and complex datasets.
Knowledge of other programming / scripting languages used in the community (C, C++, R, Matlab, Octave, Julia, etc.) is beneficial.
Knowledge or willingness to learn geographical information system and related technologies (databases).
Experience in implementing, training and evaluating machine and deep learning models for spatio-temporal data.
Positive attitude towards interdisciplinary collaboration, and willingness to frequently interact with the other team members and domain scientists.
Interest in snow, avalanches, mountains, natural hazards is a plus.

Workplace

We offer

A stimulating, collaborative, diverse and cross-disciplinary environment spread across several institutions (the position is hosted at ETH Zurich, but SDSC has offices at the EPFL and the Paul Scherrer Institut).
We value work-life balance.
Possibility to publish your research in top-ranked conferences and journals.
Possibility to travel and present your work at different national and international events.
Help supervise students and participate in lectures.

chevron_right Working, teaching and research at ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

Curious? So are we.

We look forward to receiving your online application with the following documents:

Letter of Motivation
Curriculum Vitae
Electronic copy of your academic diplomas, grades and certificates
Contact details from at least 2 but no more than 3 references

Further information about Swiss Data Science Center can be found on our Website. Questions regarding the position should be directed to Dr. Michele Volpi, michele.volpi@sdsc.ethz.ch (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

About ETH Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

Wenn Sie sich auf diese Stelle bewerben, erwähnen Sie bitte dass Sie sie auf Universitoxy gesehen haben.

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