Instructor (Ocean Observation); School of Ocean Technology

Job Title: 
Instructor (Ocean Observation)
Job Location: 
St. John's, NL, Canada
Application Deadline: 
Wednesday, December 16, 2020

The School of Ocean Technology (SOT) ( ) is part of the Fisheries and Marine Institute of Memorial University, Canada’s most comprehensive centre for education, training, applied research and industrial support for the ocean industries. SOT delivers programs at the diploma, bachelor and graduate level, meeting the needs of this sector by focusing on key technological areas such as ocean characterization, instrumentation and observation and autonomous vehicle technology.  As new innovations continue to shape ocean industries, SOT will continue to play a critical role in educating and training highly qualified people. These personnel will lead research and development initiatives supporting industry growth while also engaging the community. 

SOT pays significant attention to ocean characterization and intervention in harsh environments through directed academic and research programming focused on efforts in ocean observation, vehicle technologies and ocean mapping.  SOT is routinely involved in the integration, testing and customization of sensors to meet unique environmental and data challenges. Through its Centre for Applied Ocean Technology, SOT leads the SmartAtlantic (SmartBay) Ocean Observation initiative, one of the largest and longest standing ocean observation initiatives in Canada. Areas of opportunity can be found in the limitations of existing technology and the voids in areas such as power generation and energy efficiency; cost effective sensor to shore; sensor to ship communications; sensor development and integration; data management; and efficient and effective information dissemination on a real-time basis.  The complexities of the Northwest Atlantic and its atmospheric interface not only challenge our understanding of the role of the oceans and the influences of climate change but also create huge challenges and uncertainties for those tasked with operating in such environments.  Recognizing the data voids and developing new and innovative approaches to data modelling and analysis are all part of this challenge.

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