Jobs:Job-00757
Start reviewing process: 9 September 2022
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Posting:
Position: PhD opportunities
Start reviewing process: 9 September 2022
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These projects are fully funded (including full tuition support, a stipend, health insurance, and travel support for conference attendance), supported by National Science Foundation and National Oceanic and Atmospheric Administration. A successful candidate will be supervised by Dr. Hamed Moftakhari in the Department of Civil, Construction and Environmental Engineering at the University of Alabama, and is expected to conduct innovative and applied research on compound hazard assessment and forecasting using coupled hydrologic-hydrodynamics models and probabilistic methods. 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 compounding effects of hydroclimate hazards (including pluvial, fluvial, and coastal) pose threat to coastal communities. Projects 2 and 3 are related to Cooperative Institute for Research to Operations in Hydrology (CIROH) recently funded by NOAA (https://news.ua.edu/2022/04/ua-awarded-360-million-to-lead-national-water-effort/).
All positions are available as soon as January 2023. A qualified applicant must have strong quantitative and analytical skills, and holds a MSc in Civil, Environmental, or Ocean Engineering (or related disciplines), and strong written and oral communication of research results. Members of underrepresented groups in STEM are particularly encouraged to apply. For consideration, please submit your CV, a sample technical writing, a cover letter explaining your research experience/interests and list of 3 references to Dr. Hamed Moftakhari (hmoftakhari@eng.ua.edu). In subject line “CHL-PhD Application”.
PhD research position #1: Nonstationary compound flood frequency analysis Desired qualifications include experience in probabilistic modeling, statistical methods, flood frequency analysis and hydrodynamic modeling (e.g., DFLOW-FM, ADCIRC, ADH, SCHISM). The successful candidate will be expected to develop novel statistical methods suitable for compound flood hazard assessment under nonstationarity.
PhD research position #2: Machine Learning for compound flood assessment Desired qualifications include experience in hydrodynamic modeling (e.g., DFLOW-FM, ADCIRC, ADH, SCHISM) and development of machine learning algorithms. The successful candidate will be expected to set up detailed hydrodynamics models of select coastal/estuarine systems, assist with integrating distributed hydrologic models (i.e. National Water Model) and hydrodynamics model and developing machine learning algorithms for comprehensive compound flood assessment and generating flood maps.
PhD research position #3: Nature-based solutions for compound flood impact mitigation
Desired qualifications include experience in hydrodynamic modeling, coupled model parametrization and validation, familiarity with basics of nature-based solutions. The successful candidate will be expected to assist with hydrologic-hydrodynamic-biologic model coupling and set up, including the integration of distributed hydrologic models (i.e. NWM) with a detailed hydrodynamics model of select estuaries and biologic model of wetlands that simulated the response of these natural features to hydroclimate forcings and anthropogenic effects; and assess the performance of these natural features against compound coastal floods.
