Durée : 3 years
The Scale-Aware Sea Ice Project (SASIP), aims at developing a truly innovative, scale-aware continuum sea ice model for climate research; one that faithfully represents sea ice dynamics and thermodynamics and that is physically sound, data-adaptive, highly parallelized and computationally efficient. SASIP will use machine learning (ML) and data assimilation (DA) to exploit large datasets obtained from both simulations and remote sensing.
Through the further development of existing important state-of-the-art simulators created by some of the investigators, SASIP will build a data-constrained sea ice model that is based on solid-like physics. This model will allow improved high resolution and large scale predictions of Arctic and Antarctic sea ice, and the propagation of sea ice related climate feedback. Employing hybrid DA and ML approaches as a native part of the model architecture will allow for objective combinations of models and data. Ultimately, SASIP will give a better understanding of the impact of amplified warming in polar regions through the development of a model that reduces uncertainties related to global earth systems. SASIP is an international project funded and supported by the Schmidt Futures foundation: https://schmidtfutures.com/our-work/scientific-knowledge. Link to the SASIP website: https://sasip-climate.github.io CEREA is a joint laboratory of École des Ponts ParisTech and EdF R&D and a member of Institut Pierre-Simon Laplace (IPSL), and has an internationally recognized expertise in data assimilation and machine learning applied to the geosciences. CEREA will lead Task 4.3 of SASIP WP4, which is entitled Data Assimilation and Machine Learning, and will strongly interact with other members of WP4 and of the whole SASIP team.A PhD position (3 years) and/or a postdoc fellow position (at least 2 years) is offered at ENPC/CEREA to work on Task 4.3.
Send an email to Prof. Marc Bocquet (firstname.lastname@example.org) with CV and motivation letter.
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