This NSF-funded project seeks to use 'big data' generated by new tools and technologies to enhance our ability to predict development of harmful cyanobacterial blooms (HCBs, see photo at right) through adaptive sampling of lakes with incipient cyanobacterial blooms. While the ultimate causes of HCBs have been well characterized, identifying the proximate triggers of these events and forecasting incipient blooms are still major challenges due in part to the limited spatial and temporal resolution of the available data and lack of real-time or near-real-time data integration.
Members of the team come from six institutions (Dartmouth, University of New Hampshire, Bates, Colby, University of Rhode Island, and University of South Carolina) within four EPSCoR jurisdictions (New Hampshire, Maine, Rhode Island, and South Carolina). Areas of expertise include big data, environmental science, ecology, instrumentation, and robotics.
During 2020-21, our team will add two postdocs, a graduate student, and a number of summer undergraduate researchers:
- A postdoctoral researcher at Dartmouth with expertise in quantitative modeling and machine learning (to be supervised by Alberto Quattrini Li, VS Subrahmanian, and Kathy Cottingham)
- A postdoctoral researcher based at Bates and also working with Colby College participants with expertise in limnological data collection and modeling (to be supervised by Holly Ewing and Denise Bruesewitz)
- A Dartmouth graduate student, focusing on limnology (to be supervised by Kathy Cottingham).
- Summer undergraduate research students, to be based at any of the 6 institutions and with interests in any of the primary research fields or the disciplinary seams between fields (contact anyone listed above, plus Mike Palace, Paolo Stegagno, Yiannis Rekletis, and Annie Bourbonnais).
The exact position descriptions are in development. To signal your interest in learning more about each position, contact the individual(s) linked above.