Join a groundbreaking research project focused on improving avalanche forecasting using machine learning and physics-based models. You will work with a multidisciplinary team at the Swiss Data Science Center (SDSC) in Zurich, collaborating closely with experts from the WSL Institute for Snow and Avalanche Research (SLF), ETH Zurich, and other institutions. This project aims to develop intelligent, high-resolution spatiotemporal models to support expert forecasters and enhance public safety.

• Carry out independent research and propose creative solutions for modeling and forecasting snow, climate, and avalanche processes using advanced machine learning techniques.
• Design, develop, and implement baseline machine learning models, including those that integrate physics-based knowledge.
• Build and improve multivariate time series forecasting models.
• Work collaboratively on codebases with project members and open source contributors.
• Engage and communicate effectively with experts from diverse backgrounds, including machine learning, physics, natural hazards, and software engineering.
• Present scientific findings to both specialist and general audiences.
• Prepare scientific publications and present at seminars and workshops.
• Engage with institutional teams and contribute to the broader project goals.

• Master's degree in computer sciences, natural sciences, or a related field (e.g., physics, geography, environmental sciences, environmental engineering, natural hazards).
• Ability to quickly understand new physical concepts, from theory to numerical simulations.
• Strong skills in mathematical problem-solving and translating solutions into scientific code.
• Experience in applied machine learning research and solving real-world problems.
• Proven ability in scientific programming and prototyping in Python, especially with PyTorch and other machine learning libraries.
• Commitment to open research, reproducibility, and high standards.
• Experience with large and complex datasets is a plus.
• Knowledge of additional programming languages (such as C, C++, R, Matlab, Octave, Julia) is beneficial.
• Willingness to learn about geographical information systems and related technologies.
• Experience with machine and deep learning models for spatio-temporal data is an advantage.
• Positive attitude towards interdisciplinary collaboration and frequent interaction with team members and domain scientists.
• Interest in snow, avalanches, mountains, or natural hazards is a plus.

• Work in a stimulating, collaborative, and cross-disciplinary environment across leading Swiss institutions.
• Support for work-life balance.
• Opportunities to publish research in top conferences and journals.
• Travel to present your work at national and international events.
• Participate in student supervision and lectures.
• Be part of an inclusive culture that values diversity, equality, and sustainability.