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The purpose of this Funding Opportunity Announcement (FOA) is to support investigative and collaborative research focused on developing and evaluating simulation modeling and systems science to understand and address minority health and health disparities.

The purpose of this notice of funding opportunity (NOFO) is to support a research project that aims to develop and implement an influenza-like illness (ILI)-specific student absentee monitoring system in kindergarten through twelfth grade (K-12) schools and assess its usability for early detection of influenza, SARS-CoV-2, and other respiratory pathogen transmission in schools and surrounding communities. To achieve this aim, the project team will: 1) rapidly determine the causes of school absenteeism in students across selected school district(s) over a three-year period; 2) detect within-household transmission of influenza and SARS-CoV-2 in households from which a student has been absent from school due to ILI; and 3) assess comparability between influenza-specific and SARS-CoV-2-specific student absenteeism data from the participating schools and multiple layers of complementary influenza and SARS-CoV-2 surveillance data routinely collected in the health care facilities serving the general population of this school district.

NineSigma, representing the Electric Power Research Institute (EPRI), invites proposals from organizations and companies for technologies and digital tools to provide real-time feedback from various sensory inputs for adaptive welding applications The ideal solution will collate and analyse data from the following inputs. Real-time imaging data from vision systems Real-time weld, groove and joint profile data from laser profiler Weld parameter data acquisition Acoustics and vibration (accelerometer) data analysis

The Laura Bassi Scholarship was established by Editing Press in 2018 with the aim of providing editorial assistance to postgraduates and junior academics whose research focuses on neglected topics of study, broadly construed, within their disciplines.

This fall, OARS will host a series of brown-bag workshops on navigating the NSF proposal process.  Workshops will be held select Tuesdays from noon to 1:00pm in Pearson 208.  You are welcome to attend any or all of the sessions.  Click through for dates and registration information.

NSF's Harnessing the Data Revolution (HDR) Big Idea is a national-scale activity to enable new modes of data-driven discovery that will allow fundamental questions to be asked and answered at the frontiers of science and engineering. Through this NSF-wide activity, HDR will generate new knowledge and understanding, and accelerate discovery and innovation. The HDR vision is realized through an interrelated set of efforts in: Foundations of data science; Algorithms and systems for data science; Data-intensive science and engineering; Data cyberinfrastructure; and Education and workforce development. Each of these efforts is designed to amplify the intrinsically multidisciplinary nature of the emerging field of data science. The HDR Big Idea will establish theoretical, technical, and ethical frameworks that will be applied to tackle data-intensive problems in science and engineering, contributing to data-driven decision-making that impacts society.

NSF's Harnessing the Data Revolution (HDR) Big Idea is a national-scale activity to enable new modes of data-driven discovery that will allow fundamental questions to be asked and answered at the frontiers of science and engineering. Through this NSF-wide activity, HDR will generate new knowledge and understanding, and accelerate discovery and innovation. The HDR vision is realized through an interrelated set of efforts in: Foundations of data science; Algorithms and systems for data science; Data-intensive science and engineering; Data cyberinfrastructure; and Education and workforce development. Each of these efforts is designed to amplify the intrinsically multidisciplinary nature of the emerging field of data science. The HDR Big Idea will establish theoretical, technical, and ethical frameworks that will be applied to tackle data-intensive problems in science and engineering, contributing to data-driven decision-making that impacts society.