Motivated and hardworking individuals, who are seeking graduate studies in HydSens Laboratory, are encouraged to contact Ardeshir Ebtehaj (e-mail: ebtehaj@umn.edu).
NEW OPPORTUNITY (Fall 2022)
Data Assimilation of Satellite Precipitation: One Postdoc/PhD position is available at the Saint Anthony Falls Laboratory (SAFL) and the Department of Civil Environmental and Geo- Engineering at the University of Minnesota – Twin cities. The position will be funded by the NASA’s Precipitation Measurement Mission (PMM) program and needs to be focused on the state-of-the-art research and development on data assimilation of satellite precipitation into a coupled land-atmosphere model for improved forecasts of mesoscale convective systems. The project involves developing low-order models for precipitation moist processes through modern machine learning tools and expanding the previous research of the team on the Ensemble Riemannian Data Assimilation over the Wasserstein Space. Candidates with previous experiences, or a strong desire to learn, remote sensing hydrology, data assimilation, statistics, optimization, machine learning are encouraged to apply. The project will provide a unique opportunity to attend annual PMM science team meetings and exchange ideas with the science team members.
The postdoc position will be available in February 2022. The PhD position is expected to start in Fall 2022. For consideration, please submit only your CV to Ardeshir Ebtehaj. The CV must include a list of at least 2 references.
Note: This position is currently filled. I have no additional available position until further notice.
Current Projects:
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Extending Forecast Skills of Global Precipitation: A Deep Learning Framework for IMERG Data Assimilation over the Wasserstein Space, NASA PMM science team, 2022-2024 (PI).
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Remote Sensing and Super-resolution Imaging of Microplastics in Surface Waters (LCCMR), 2021-2024 (PI)
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Metric Learning for Joint Inversion of Land-atmosphere Radiative Transfer Equations: Improved Microwave Remote Sensing of Cryosphere and Atmosphere (NASA), 2020-2023 (PI)
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Improving Passive Microwave Retrieval of Snowfall and Snowpack on Ice-covered Surfaces, National Aeronautics and Space Administration (NASA), 2020-2023 (PI).
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Physically Constrained Inversion of the First-order Radiative Transfer Equations for High-resolution Retrievals of Soil Moisture and Vegetation Water Content using SMAP Data, National Aeronautics and Space Administration (NASA), 2019-2022 (PI).
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Reducing Uncertainties in GPM Snowfall Retrievals: Applications for Improved Prediction of Snowstorms, National Aeronautics and Space Administration (NASA), 2018-2021(PI).
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Robust Variational Data Assimilation under Incomplete and Inaccurate Data: Extremes, Biases, and Observability in Joint Assimilation of Satellite Precipitation and Soil Moisture, National Aeronautics and Space Administration (NASA), 2018-2021(PI).
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Soil Moisture Super-resolution and Regularized Data Assimilation: Algorithms and Hydro-agronomic Application in SMAP Era, National Aeronautics and Space Administration (NASA), 2016-2019 (Co-PI).
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Advanced Inversion Algorithms for GPM Passive Microwave Retrievals and Multi-sensor Merging, National Aeronautics and Space Administration (NASA), 2016-2019 (Co-PI).