Open position for a qualified and motivated PhD student to conduct research to enhance cyanobacteria bloom prediction capabilities in freshwater ecosystems through a fusion of field experimentation, mechanistic modeling, and machine learning techniques. The anticipated start date is the Fall 2021 academic semester, but applications for Summer 2021 or Spring 2022 academic semester will also be considered. The student will pursue their graduate degree in Biological & Agricultural Engineering, but will also have the opportunity to collaborate with project investigators in computer science, robotics, civil engineering, biology, as well as USGS water scientists.
Project Overview: Cyanobacteria are ubiquitous to freshwater ecosystems; however, increasingly frequent blooms of noxious and potentially toxic cyanobacteria, or cyanoHABs, and their associated cascade of ecological, social and economic impacts drive the need to better understand and predict the occurrence of cyanoHABs worldwide. In this project, we aim to couple mechanistic and data-driven modeling approaches to provide a roust prediction framework that overcomes the site-specific nature of existing cyanoHAB models and that enables causative factors to be more readily identified and managed. The selected graduate student will be part of an interdisciplinary team encompassing environmental and ecological engineering, robotics and environmental sensing, data science, and aquatic ecology.
Responsibilities: Duties will include collection and analysis of water quality data from field sites in Kansas and New York, operation and maintenance of monitoring instrumentation, model creation and calibration, data analyses, and preparation of peer-reviewed manuscripts. The student is expected to make meaningful contributions to mechanistic modeling efforts, but will also be encouraged to collaborate with data scientists on the team to integrate machine learning and other data-driven modeling techniques.
- Previous degree (B.S. and/or M.S.) in engineering from an ABET-accredited institution and background in one or more of the following areas: water chemistry, agricultural/biological, ecological or environmental engineering, hydrology, environmental science, or related field
- Excellent written and oral communication skills
- The ability to participate in field projects in variable weather conditions (instrumentation installation and upkeep, data collection, etc.)
- Previous experience related to the above project (e.g., modeling, water quality monitoring, data science)
- Enthusiasm for research
To apply: The assistantship includes tuition waiver, health insurance, and stipend. Interested students may apply by sending their resume and a cover letter stating their interest and qualifications to both Dr. Trisha Moore (email@example.com) and Dr. Aleksey Sheshukov (firstname.lastname@example.org).