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NEW OPPORTUNITY (Fall 2023)

Multiple Ph.D. and Postdoctoral Positions in water quality modeling of lakes, climate change, and Snowfall Remote Sensing, the University of Minnesota Twin Cities

Work Environment: The Hydrologic Sciences and Remote Sensing (HydSens) group, led by  Ardeshir Ebtehaj, is part of the Civil, Environmental, and Geo-Engineering Department and the Saint Anthony Falls Laboratory at the University of Minnesota Twin-Cities. The team works on several research areas including passive microwave remote sensing of global precipitation and soil-snow-vegetation continuum and novel methodologies and mathematical frameworks for improved short and long-term weather and hydrologic forecasts through data assimilation and deep learning. For more information, please visit our website at https://www.hydsens.com/

Project and Positions: Dr. Ebtehaj has been selected to serve as the Principal Investigator on a NASA-funded project titled “A Multi-Decadal Satellite Snowfall data RecOrd (MAESTRO)”. The project is collaborative between the University of Minnesota, Goddard Space Flight Center, and the Jet Propulsion Laboratory. The project also has collaborators in the National Oceanic and Atmospheric Administration, the Institute of Atmospheric Sciences and Climate (CNR-ISAC) in Italy, and the Tokyo Institute of Technology. The project scope includes funding for multiple Ph.D. and Postdoctoral positions that will work closely with Dr. Ebtehaj, and other collaborators of the projects. This project aims to use a constellation of NASA and NOAA satellites to provide the longest data record of global snowfall to better understand how the precipitation phase has changed in the past four decades in response to global warming. The position will consist of yearly contracts, renewable for up to five years depending on funding availability, adequate research performance, and positive contributions to the project.

Project and Positions: I am looking for qualified Ph.D. and Postdoc candidates in the field of rainfall-runoff and water quality modeling of lakes and river systems under super-resolution climate change scenarios.    

Required skills and prerequisites: Background in Civil and Environmental Engineering, Hydrology, Earth/Atmospheric Science, Computer Science, Physics, and Applied Mathematics. Strong writing and communication skills are also required, as is the ability to work independently and collaboratively. Experience in programming and analyzing data in MATLAB, Python, or a similar technical programming language is required. Past experiences in precipitation processes, microwave and infrared remote sensing, satellite data analysis, radiative transfer modeling, Bayesian and frequentist statistics, linear and non-linear optimization, machine learning, and neural networks are advantageous.   

Applicants should send the following materials to Ardeshir Ebtehaj at ebtehaj@umn.edu: CV, and contact information of at least two references. The result of the TOEFL test is needed for most international Ph.D. applicants. Applicants are highly encouraged to send the results of any available standardized test that measures the general mental ability of an individual such as the GRE test – especially the quantitative part.  The start date is immediate (Fall 2023) but can be flexible.

 

Note: Unfortunately, I cannot reply to all received applications. I will reach out only to those applicants who fit the required qualifications. 

Projects:

  1. Hyperspectral Characterization of Toxic Harmful Algal Blooms, (LCCMR), 2024-2028 (PI)

  2. The Fate of Minnesota Lakes in the Next Century, (LCCMR), 2024-2028 (PI)

  3. A Multi-Decadal Satellite Snowfall data RecOrd (MAESTRO), NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2023-2028 (PI)

  4. 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).

  5. Remote Sensing and Super-resolution Imaging of Microplastics in Surface Waters (LCCMR), 2021-2024 (PI)

  6. Metric Learning for Joint Inversion of Land-atmosphere Radiative Transfer Equations: Improved Microwave Remote Sensing of Cryosphere and Atmosphere (NASA), 2020-2023 (PI)

  7. Improving Passive Microwave Retrieval of Snowfall and Snowpack on Ice-covered Surfaces, National Aeronautics and Space Administration (NASA), 2020-2023 (PI).

  8. 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).

  9. Reducing Uncertainties in GPM Snowfall Retrievals: Applications for Improved Prediction of Snowstorms, National Aeronautics and Space Administration (NASA), 2018-2021(PI).

  10. 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).

  11. 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). 

  12. Advanced Inversion Algorithms for GPM Passive Microwave Retrievals and Multi-sensor Merging, National Aeronautics and Space Administration (NASA), 2016-2019 (Co-PI).

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