Opportunities and Projects

NEW OPPORTUNITY (Fall 2025)

Ph.D. or Postdoctoral Positions in remote sensing of the Arctic landscape at 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: I have one position available for Fall 2025. Please see the description in this LinkedIn posting.

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 and Spring 2024) 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.

Minneapolis Campus

SAFL

Mall Area

Northrop Memorial

Downtown view

UMN TCF Stadium

Projects:

High-Fidelity Modeling of Net Ecosystem Exchange Responses to Winter and Shoulder Season Heatwaves in the Arctic and Boreal Zone, 2025-2028 (PI).
Hyperspectral Characterization of Toxic Harmful Algal Blooms, (LCCMR), 2024-2028 (PI)
The Fate of Minnesota Lakes in the Next Century, (LCCMR), 2024-2028 (PI)
A Multi-Decadal Satellite Snowfall data RecOrd (MAESTRO), NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2023-2028 (PI)
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).
Remote Sensing and Super-resolution Imaging of Microplastics in Surface Waters (LCCMR), 2021-2024 (PI)
Metric Learning for Joint Inversion of Land-atmosphere Radiative Transfer Equations: Improved Microwave Remote Sensing of Cryosphere and Atmosphere (NASA), 2020-2023 (PI)
Improving Passive Microwave Retrieval of Snowfall and Snowpack on Ice-covered Surfaces, National Aeronautics and Space Administration (NASA), 2020-2023 (PI).
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).
Reducing Uncertainties in GPM Snowfall Retrievals: Applications for Improved Prediction of Snowstorms, National Aeronautics and Space Administration (NASA), 2018-2021(PI).
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).
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).
Advanced Inversion Algorithms for GPM Passive Microwave Retrievals and Multi-sensor Merging, National Aeronautics and Space Administration (NASA), 2016-2019 (Co-PI).