Applications are invited for a 3-year position in a Research Fellowship as PhD Candidate in Geosciences to be based at the Njord Centre, a cross-disciplinary research unit at the interface between Physics and Geosciences at the University of Oslo.
In this project, you will use machine learning to develop and optimize molecular scale rock models that capture mechanical properties of rocks and rock–water interactions simultaneously. You will use these models to understand dynamic fracture of rocks and the role of water in rock fracture.
To keep model simulations closely tied to observations, we will develop methodology to update model states on-the-fly fed by near real-time datastreams.
Applications are invited for a 3-year position in a Research Fellowship as PhD Candidate in Geosciences to be based at the Njord Centre, a cross-disciplinary research unit at the interface between Physics and Geosciences at the University of Oslo.
In the project, we will apply a range of maximum principles to various problems, from atmospheric heat transport (particularly the “Arctic amplification” of global warming) to the density stratification of the ocean.
This project will aim for modelling the global and regional climate effects of major Icelandic eruptions with different SO2 emission strengths and explosivity focusing on radiation, clouds, hydrology, circulation and Earth System coupling processes at high latitudes. High Performance Computing platforms and cloud services are key for the training in computational climate science.
In this project we will utilize the growing database of global satellite remote sensing observations, along with advanced signal processing techniques, to make much improved scale estimates of a variety of dynamical phenomena in the Earth system.
This project aims to use sophisticated computational methods for improving our knowledge of the location and characteristics of submarine volcanoes.
Convection within Earth’s mantle interior depends on rock deformation at depth. Recently this deformation has been shown to be anisotropic. This project will develop methods to incorporate this anisotropy into computer models of mantle convection.
Silicate magmas are the major connection between the heat factory of the deep Earth and the incessantly changing crust. Occasionally magmas brought large amounts of material from the mantle to the surface. These large-scale eruptions altered climate, tectonic isostasy, and influenced biosphere.
Molecular water enters the mantle in subduction zones, and affects the state, history, and dynamics of the mantle. This project will use numerical simulations to explore the mechanism for hydrogen diffusion in the mantle, and its impact on overall mantle dynamics.
The PhD candidate will investigate the role of apparently random, small-scale, natural fluctuations of atmospheric processes to trigger stably stratified boundary layer regime transitions.
This project aims to study the predictability of deformations in small crystals using machine learning models with data from phase field crystal (PFC) model for plasticity.
