SBIR Phase II: Low-cost, Wireless, Energy Harvesting Environmental Sensors
This Small Business Innovation Research (SBIR) Phase II project focuses on the development of a low-cost, wireless, and energy harvesting environmental sensor, and a data aggregation / visualization platform to enable effective communication of data to users and control systems. This project addresses major barriers in our main market, the application of IoT to the buildings sector, where complexity is encountered in the placement and powering of sensors in monitoring locations where access is limited. By reducing the upfront and maintenance costs of wireless sensor networks, the project allows cost-effective building sensor data collection of unprecedented longevity and density. These rich data sets in turn enable myriad benefits, including more effective building management controls and in-depth automated energy evaluations. The commercial availability of a low-cost, wireless, energy-harvesting environmental sensor would enable high-granularity sensing, feedback, and control to several additional markets including industrial, agricultural, and any other market application that would benefit from monitoring where power or access is in short supply.
This Small Business Innovation Research Phase II project focuses on developing a low-cost, wireless, energy-harvesting environmental monitor using commercially available components and standard processes. An energy harvesting sensor of this type is not available commercially, and is uniquely enabling for building energy auditing and controls, supporting a new generation of automation and systems integration while eliminating the cost of extensive powered sensor installation or battery maintenance. This device was developed through Technology Readiness Level (TRL) 7 in Phase 1 of the SBIR program, in parallel with supporting data infrastructure, including dynamic cloud databases, data access via API methods and a beta data visualization platform. Phase II of the program aims to expand the scope of the low-cost, wireless, energy-harvesting environmental sensor from a network of low-power, short-range devices to a plug-and-play building-wide sensing solution, which includes the incorporation of additional hardware elements, including routers to extend the range of the low-power devices, and the development of automated data analysis strategies to extract valuable building information for the end-user.
This Small Business Innovation Research (SBIR) Phase II project focuses on the development of a low-cost, wireless, and energy harvesting environmental sensor, and a data aggregation / visualization platform to enable effective communication of data to users and control systems. This project addresses major barriers in our main market, the application of IoT to the buildings sector, where complexity is encountered in the placement and powering of sensors in monitoring locations where access is limited. By reducing the upf...
A Postdoctoral Fellowship position is available at Radiator Labs, a technology company located in Brooklyn, New York, developing an advanced technology demonstrated to reduce the energy use in steam-heated buildings by up to 40%. This position, funded by the National Science Foundation, will develop the tools and theory to examine Radiator Labs' unique building information dataset and develop actionable insights relating to building operation and energy efficiency. The candidate will also help develop tools to automate building processes and diagnostics by applying machine learning and data mining techniques.
In addition to examining topics of immediate interest to Radiator Labs, the candidate will have the opportunity to develop their own research topics in smart buildings and IoT. The successful candidate will lead the analysis, presentation, and publication of their research, and contribute to the general day-to-day operations of the company.
PhD in computer science, electrical engineering, applied mathematics, or a similar discipline.
Strong interest in research and learning new technologies.
Proficient at writing technical papers/reports/presentations.
Experience with Python.
Ability to work with large data sets, cloud-based development and deployment, open source practices is a plus.
Applicants must be U.S. citizens, U.S. nationals or U.S. permanent residents.
Applicant must have received a Ph.D. degree in the past 7 years.
Experience in methods, theory, and application of machine learning and data mining.
Experience with data presentation and visualization.
Experience with databases and time series data.
Experience with streaming data analysis.
Small Business Postdoctoral Research Diversity Fellowship Program
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