Ph.D. - ENVE
Parisa Sarzaeim was qualified to attend the University of Tehran (UT) and began her Bachelor of Science in Agricultural Engineering-Water Resources Management in 2010. The most important reason to choose this major is her interest in identifying water-related issues, which are considerable worldwide and looking for novel practical solutions to tackle them. In 2014, she was honored to be selected as an exceptionally talented student among undergraduate students and became qualified to receive a merit-based admission offer to M.Sc. program without taking the national entrance exam in Water Resource Engineering at the University of Tehran. According to various environmental problems and water scarcity, in her MSc, she decided to investigate climate change effects on water resources, particularly rivers, and assess environmental state compared to other critical demanding sectors, especially agriculture. Her thesis titled "Determination of reservoir environmental demand allocation rules under climate change conditions" has been selected as an outstanding M.Sc. thesis in the 27th Research Festival-University of Tehran in 2018. Besides her academic career, she worked in a consulting engineers company and got involved in several hydrological and water resources projects. These projects provide many valuable experiences for her and make her familiar with the actual water challenges. According to her background, she believes that our water-related issues are widespread, and therefore, efficient and multidisciplinary cooperation is required to recognize them accurately. She thinks it is the first step to reach a solution. Thus, in 2018 she joined the Biological Systems Engineering graduate program at the University of Nebraska-Lincoln (UNL), an excellent opportunity to work and focus on functional areas in her favorite field. Now, she is developing a conceptual framework for crop phenotypes predictability improvements with respect to environmental variables. Her other projects include multidimensional database management and leading a cross-functional team to design a Python-based software pipeline for maize phenotypic prediction.