PhD Student in Computational Snow Physics (100%)

The project: Snow is a highly dynamic porous material mainly made from ice. As the snow structure evolves via so-called snow metamorphism, so evolve macroscopic properties as e.g., radiative properties relevant for surface energy balances and remote sensing. However, the increased understanding of snow processes has not yet found its way into adequate representations at larger scales. One reason is the lack of sufficiently complete metamorphism models that allow for a direct link between ice crystal growth, sublimation, sintering, and bulk properties; another reason is the contrast between empirical approaches of classical snow science versus methods based on fundamental physics.

Within a project financed by the Swiss National Science Foundation SNF, we strive to develop a versatile snow metamorphism model that is based on the phase-field methodology and to couple it to radiative transfer models. Comparison with data acquired in paralleling projects running at WSL/SLF and EPFL and analyses using machine learning shall be used to develop upscaling techniques and parametrizations for large scale predictions, e.g., in the context of photovoltaic installations in complex, snow covered terrain. 

Your responsibilities:

• Formulate a phase-field model that can predict close to isothermal snow metamorphism. In this case, the process can be interpreted as sintering, whereby intergranular and grain-boundary forces must be adequately considered. Rigid body forces, i.e., movement of individual ice crystals, will present additional challenges. 
• Parametrize the model to represent ice physics as accurately as possible or calibrate parameters using available experimental data.
• Perform sensitivity analyses to determine dominant driving forces and identify model simplifications while taking strong sensitivities adequately into account.
• Apply the model to small snow samples obtained from ?-CT and confront modeling results with existing measurement series.
• Contribute to the overarching project goals by collaborating within the 3-person project team and the partner institutes.
• Contribute, to a small extent, to teaching and tutoring at OST or EPFL in the context of the doctoral school. 

Your qualifications:

• Masters degree in physics, materials science, applied mathematics or equivalent
• Strong background in and flair for mathematical modelling and numerical simulation
• Interest in small-scale processes and their impact on large-scale properties, ideally linked to snow or other porous materials
• Eager to work in a multi-disciplinary context and multi-lingual environment and to collaborate with national and international partners
• Motivation to work at the interface between fundamental and applied sciences

We provide:

• An independent job that offers both scope for personal initiative and the opportunity to exchange ideas with committed colleagues
• A family environment at an applied sciences university with attractive employment conditions
• Collaboration opportunities with SLF Davos, EPFL, and École Polytechnique Paris
• An attractive workplace on the shore of lake Zurich and in immediate vicinity of the Rapperswil train station and historic (small) town

Have we aroused your interest and do you have the desired qualifications? Then send us your complete application documents until 1st of January 2025 or upon arrangement using our online tool (jobs-ost.ch).

For further information, please contact Prof. Dr. Thomas Kaempfer, OST, Write an email, +41 79 626 46 37 or, for specific questions on the EPFL doctoral school, Prof. Dr. Michael Lehning, EPFL/SLF, Write an email