PhD Candidate in Sea-Ice Physics and Modelling

About this job

https://youtu.be/Xt-yHCN5QS0 About the position We invite applications for a PhD position in sea-ice dynamics and numerical modelling, funded through Arctic Ocean 2050 — Norway’s national, decade-long flagship program for Arctic Ocean research. The PhD project will advance discrete element modelling (DEM) approaches for sea ice, with a focus on ridging processes and wave–ice interaction in the Marginal Ice Zone (MIZ), while also addressing computational efficiency and scalability. The research will primarily build on and extend the SAMS (Simulation of Arctic Marine Systems) framework, using it as a platform for developing new sea-ice physics and modelling strategies relevant for next-generation Arctic prediction, monitoring, and decision-support systems. The successful candidate will join a strong interdisciplinary research environment, gain access to advanced modelling tools and national computing infrastructure, and contribute to a high-impact research programme with strong relevance for Arctic decision support. Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia. Your immediate leader will be a professor. About the project Rapid Arctic warming is driving profound changes in sea-ice extent, thickness, and mechanical behavior, with wide-ranging implications for climate feedbacks, ecosystems, infrastructure, navigation, and geopolitics. Despite major advances, large-scale continuum sea-ice models still rely on simplified parameterizations to represent critical sub-grid physical processes such as ridging, floe interaction, jamming, and wave-induced breakup. Discrete Element Models (DEMs) offer a powerful complementary framework by explicitly resolving ice floes and their mechanical interactions. By allowing deformation, fracture, and redistribution processes to emerge naturally from contact mechanics, DEMs provide a physically grounded basis for improving our understanding of sea-ice dynamics and informing more realistic parameterizations in continuum models. The overarching ambition of this PhD study is to advance DEM-based sea-ice physics in a way that bridges floe-scale mechanics, continuum sea-ice model parameterization, and operational decision support. Develop physically consistent representations of ice ridging within a DEM framework, enabling simulation of ice compression, mass redistribution, and ridge keel formation. Incorporate wave–ice interaction processes, with emphasis on wave-induced floe motion, breakup, and rearrangement in the Marginal Ice Zone. Derive emergent sea-ice properties (e.g. effective strength, deformation rates, floe-size distributions) and translate these into improved parameterizations for continuum sea-ice models used in climate and forecasting systems. Improve computational efficiency and scalability of DEM-based simulations through algorithmic optimization, hierarchical or multi-resolution approaches, and/or reduced-order methods. Apply the developed modelling framework to assess mechanical ice hazards relevant for navigation, infrastructure, and Arctic observing systems. This PhD position is an integral contribution to Arctic Ocean 2050, supporting the program’s ambition to deliver scientifically robust, scalable modelling tools for a rapidly changing Arctic Ocean. The project is particularly aligned with Research Theme 5 (RT5: Advances in Observing and Modelling), where it contributes to the development of a hierarchical, physics-based modelling framework that links global and regional continuum models with high-resolution, process-resolving simulations. By advancing SAMS as a discrete element modelling platform, the project will provide physically grounded data to improve continuum sea-ice parameterizations, support machine-learning-based model emulators, and enhance the realism of Arctic digital twins. In addition, the project supports RT2 (The Changing Arctic Ocean) by improving representation of sea-ice deformation, ridging, and MIZ dynamics; RT4 (Abrupt and Extreme Events) by enabling mechanistic studies of storm-driven breakup and nonlinear ice responses; and RT3 (Human Impact and Sustainable Use) by delivering tools for ice-related risk assessment relevant to shipping, offshore infrastructure, and monitoring operations. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out high-quality research on DEM-

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