The Center for Complex Hydrosystems Research (https://cchr.eng.ua.edu/) at the University of Alabama invites applications for multiple Postdoctoral research scholars. The positions are funded by external grants from NSF, NOAA and USACE. 4 (four) of the successful candidates will be supervised by Dr. Hamid Moradkhani (www.moradkhani.ua.edu) and 1 (one) position with Dr. Hamed Moftakhari (https://hmoftakhari.people.ua.edu) in the CCHR. Postdoctoral scholars with also be affiliated with the Department of Civil, Construction and Environmental Engineering. We use computational modeling, operations research, and a wide range of in-situ and remotely-sensed data, analytical and statistical tools/methods to characterize and quantify the extent to which coastal features contribute to the resilience of coastal communities and infrastructure against compounding effects of hydroclimatic hazards. The scholars are expected to conduct innovative and applied research on variety of subjects as detailed below. Positions are available from October 1 and the appointments can be extended to 3 years. Please send your questions to email@example.com.
Multi-scale ensemble hydrologic and hydrodynamic data assimilation for real-time probabilistic flood inundation mapping: This involves developing parallelized ensemble DA applicable for both hydrologic and hydrodynamic model and simulation platform using high performance computing, and quantifying different uncertainties in hydrologic and hydrodynamic modeling.
Compound flood inundation mapping and nonstationary coastal flood hazard assessment: Characterizing the boundary conditions for hydrodynamic modeling of compound floods requires a comprehensive understanding of the dynamic interactions between atmosphere, ocean and land. Dynamic and statistical approaches for compound flood modeling will be developed or implemented.
Machine learning to predict river discharge and for coastal flood assessment
Multiple meteorological forcing data and observational data including in-situ and satellite imagery, and land surface features as well as variety of deep learning methods will be employed for river discharge prediction Also, develop physics-informed machine learning algorithms with the help of remotely sensed data and efficiently parametrizes compound flood hazard models that go beyond point water level metrics for calibration and is flexible enough to combine various spatially distributed metrics.
Nature-based solutions for compound flood impact mitigation: developing coupled hydrologic-hydrodynamic modeling and probabilistic methods within the Next-Gen National Water Model. Use global sensitivity analysis methods to assess the sensitivity of the hydrologic and hydrodynamic models for simulating extreme flooding.
Advanced data fusion modeling to update regional flood maps using remotely sensed data
An alternative for efficient post-event large-scale flood mapping is the application of remote sensing techniques to multispectral imagery (i.e. Landsat), radar data (i.e. SAR) and digital elevation models (DEMs). The scholar will integrate multi-source satellite-based data, hydrodynamic modeling, and data fusion techniques for large-scale land cover classification and compound flood mapping.
The successful candidates will be expected to lead and contribute in writing scientific papers, technical reports and present research results, and assist in developing proposals for external funding.
Additional Required Minimum Qualifications:
Ph.D. in Civil, Environmental, or Coastal Engineering majoring hydrology, hydraulics, or a closely related field. Postdoctoral candidates will need to have completed their Ph.D. or have it completed by the start of employment. Applicants should upload a cover letter, curriculum vitae, two first-authored writing samples and include contact information of at least three references. Please upload additional documents to the “other documents” tab as needed.
Skills and Knowledge:
Desired qualifications include experience in hydrologic or hydrodynamic modeling (e.g., NWM, DFLOW-FM, ADH, ADCIRC), model coupling, model calibration and validation, background in statistical/ probabilistic analysis, strong programming/scripting capabilities, high performance computing, and strong written and oral communication of research results.
Proven record in writing successful research proposals, and demonstrated ability of working in a multidisciplinary environment.
CV, Cover Letter (explaining your research experience and specific interest in the topical areas), and a list of 3 references.
To apply, please use the following link and if you feel you are qualified for more than one topical area, feel free to mention in your cover letter.
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