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Bayi Glacier in Qilian Mountain, China (Credit: Xiaoming Wang, distributed via imaggeo.egu.eu)

Job advertisement PhD in computational snow science

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European Geosciences Union

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PhD in computational snow science

Position
PhD in computational snow science

Employer
OST – Eastern Switzerland University of Applied Sciences logo

OST – Eastern Switzerland University of Applied Sciences

  • OST – Eastern Switzerland University of Applied Sciences, is the university of applied sciences for the six cantons of Eastern Switzerland and the Principality of Liechtenstein. We are an innovative and dynamic university with forward-thinking concepts and innovations, providing a major economic and social contribution to the development of Eastern Switzerland. With around 3,800 students spread across six Schools, 1,500 professionals pursuing executive education, and over 1,000 current research projects, we are Eastern Switzerland’s regional educational hub.
  • EPFL – École Polytechnique Fédérale de Lausanne, is Europe’s most cosmopolitan technical university. EPFL collaborates with an important network of partners, including other universities and colleges, secondary schools and gymnasiums, industry and the economy, political circles, and the general public, with the aim of having a real impact on society.
  • WSL Institute for Snow and Avalanche Research SLF in Davos studies snow, the atmosphere, natural hazards, permafrost, and mountain ecological systems.

Homepage: https://www.ost.ch/en


Location
Rapperswil SG, Switzerland

Sector
Academic

Relevant division
Cryospheric Sciences (CR)

Type
Full time

Level
Student / Graduate / Internship

Salary
according to Swiss National Science Foundation (PhD student)

Required education
Master

Application deadline
Open until the position is filled

Posted
18 October 2024

Job description

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.

Skills and expertise

  • 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