Scientist
This position will conduct computational research to characterize and model uncertainty in probabilistic rainfall and flood hazard applications. The incumbent will assist principal investigators with project management, preparation and submission of project reports, and coordination of activities among research group members and outside collaborators. The incumbent will also write and publish research articles, develop grant proposals, and assist in managing routine operations of the research group, including assisting with student advising in accordance with University policies.
Key Job Responsibilities:
- Writes or assists in developing grant applications and proposals to secure research funding
- Conducts literature reviews, prepares reports and materials, and disseminates information to appropriate entities
- Serves as a main point of contact and liaison with internal and external stakeholders providing information and representing the interests of a specialized research area
- Assists with the identification of research problems and the development of research methodologies and procedures
- Collects and analyzes research data, conducts experiments and interviews, and documents results according to established policies and procedures under general supervision and limited responsibility
- Attends and assists with the facilitation of scholarly events and presentations in support of continued professional development and the dissemination of research information
Required Qualifications: A PhD and a minimum of 3 years of postdoctoral experience in precipitation science or a related field is required. The candidate must have sufficient technical background, through a combination of education and work experience, to carry out original research using an integration of remote sensing products, weather/climate model outputs, hydrologic science, data science, and stochastic modeling.
Preferred Qualifications: Background and familiarity with precipitation measurement; precipitation uncertainty quantification and error propagation; stochastic or dynamical downscaling of climate model variables; statistics, statistical software; Python, C++, MATLAB, or other programming language, scripting; high performance and high-throughput computing; scientific proposal writing; hydrologic modeling.
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