Research Associate (Reference NAT00692)

Applications are invited for a Research Associate position in the Department of Mathematics at Imperial College London.  The position is funded by the European Research Council. You will be working on the Stochastic Transport in Upper Ocean Dynamics (STUOD) ERC Synergy Grant 2020. The STUOD project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. 

For this position you are required to hold a PhD (or equivalent) in the area of Stochastic Analysis in particular in Stochastic Partial Differential Equations, Nonlinear Filtering, Homogenisation Theory or Data Assimilation. 

More information is available here: Research Associate (Reference NAT00692)

Closing date: 18 June 2020 


Research Associate (Reference NAT00695)

Applications are invited for a Research Associate position in the Department of Mathematics at Imperial College London.  The position is funded by the European Research Council. You will be working on the Stochastic Transport in Upper Ocean Dynamics (STUOD) ERC Synergy Grant 2020. The STUOD project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics.

Job listing information

Further details are available here: Research Associate (Reference NAT00695)
 
Closing date: 26 June 2020

3 Post-doctoral positions FLUMINANCE group, INRIA Rennes, France
Study of ocean stochastic models for ensemble forecasting and data assimilation from highresolution data.
 
General description: Several three-years post-doctoral positions are opened in the Fluminance Inria team, (INRIA Rennes, France) to work within the ERC project “Stochastic Transport in Upper Ocean Dynamics” (STUOD) in collaboration with Ifremer and Imperial College London. The proposed research positions are at the crossing between Applied Mathematics, Computer Science and Physical Oceanography. The objective is to investigate specific stochastic parametrization for large-scale ocean models to model the effect of the unresolved components of the flow. The relevance of these stochastic dynamics will be explored in terms of oceanic modeling capacities as well as for data assimilation and ensemble forecastingpurposes.
 
Environment: The candidate will be hosted in the Fluminance Inria team located in Rennes (Britany) and will work in close collaboration with Ifremer Brest, Imperial College London and the Air-Sea Inria team in Grenoble. Fluminance and Air-Sea are part of INRIA (www.inria.fr), which is one of the leading research institute in Computer Sciences in France. Fluminance is also affiliated to the mathematics research institute of the Rennes University (IRMAR).These position are funded by the ERC project STUOD. Skills and profile. The candidate should have a solid background in applied mathematics and/or in fluid mechanics and/or in geophysical dynamics. She/he must have a good knowledge of Fortran/C/C++. He/She must have a PhD related to computational physics (Computational Fluid Dynamics, Numerical geophysical modeling and simulation, Data assimilation) or in applied mathematics.
 
Contact: Applicants must send their candidature (resume and letter of motivation) to Etienne Mémin; Fluminance team, INRIA Rennes-Bretagne Atlantique, email : etienne.memin@inria.fr
 

Several PhD studentships available on "Data assimilation, Learning and stochastic parametrization of ocean models through high-resolution observations", INRIA Rennes, France
Several PhD grants available on "Data assimilation, Learning and stochastic parametrization of ocean models through high-resolution observations"
 
Scientific Advisers: Etienne Mémin (etienne.memin@inria.fr), Centre Inria Rennes, Fluminance research group, Campus universitaire de Beaulieu, 35042 Rennes Cedex.
 
Context: ERC project STUOD in collaboration with Ifremer Brest and Imperial College London
 
Keywords: Data assimilation, stochastic parameterization and ocean models, Machine learning. PhD at the crossing between Statistical Machine learning, dynamical systems, ocean dynamics, data assimilation.
 
Summary:  The precise numerical simulation of geophysical flows such as the atmosphere or the ocean is becoming a crucial need in many aspects of our everyday life for it strongly impacts many environmental and economical fields. We may think, among others, to applications related to climate studies, oceanographic analysis or weather forecasting which are of paramount importance for the study of global warming, the tracking of polluting sheets or the prediction of catastrophic events. Unfortunately, the laws ruling such geophysical processes depend on state variables evolving in huge dimensional spaces with a strong scale coupling in space and time. The range of these interactions is so large that only large-scale representations of the system of interest can be simulated. In the other hand, one may have access nowadays to series of finely resolved data sequences depicting the footprint of the small- scale flow action.

 

Goals. Recently efficient stochastic parameterizations have been proposed to deal with models errors associated to such large-scale representation [2,4]. This framework provides in addition a way to handle the propagation of uncertainties along time. The noise representing the neglected scales and its dynamics have nevertheless to be specified in a way or another. In the PhDs proposed we aim at exploring several techniques to characterize this noise from time series of high resolution data such as provided by satellite or by high-resolution simulations.

Duration: 36 month.

Skills and profile: The candidates should have a solid background in applied mathematics , or fluid mechanics, or geophysics. She/he must have a good knowledge of Matlab or Python, and Fortran or C/C++. He/She must have a master degree related to fluid mechanicscomputational physics or applied mathematics.

Contact: Applicants must send their candidature (resume and letter of motivation) to Etienne Mémin, Fluminance team. INRIA Rennes-Bretagne Atlantique email : etienne.memin@inria.fr

More information: https://gdr-turbulence.universite-lyon.fr/phd-positions-on-data-assimilation-learning-and-stochastic-parametrization-of-ocean-models-through-high-resolution-observations-inria-rennes-france--151290.kjsp