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WHO CAN APPLY? I-Corps@Ohio funds will be offered on a competitive basis to teams of faculty researchers and graduate students developing institution-based technologies from Ohio colleges and universities. Under the supervision of business and entrepreneurial mentors, teams will develop market-driven value propositions and scalable business models around their technologies and attract follow on funding to support company formation and market entry. APPLICATION PROCESS The I-Corps@Ohio proposal submission process consists of five steps: 1. mandatory meeting with the appropriate TTO representative(s) at the PI's institution; 2. team selection of technology track (science and engineering or medtech); 3. registration of all team members in the online portal; 4. proposal submission; and 5. full team interview with I-Corps@Ohio program representatives. All teams are required to complete the online profile and submission questionnaire beginning October 23, 2019. Deadline to apply is January 15, 2019. The PI may complete this information or designate another member of the team as the lead member. Subsequent members of the team will be invited to join by the lead member through the application portal and must complete his or her profile. Every effort should be made to identify all team members prior to submitting the online proposal submission questionnaire. Additional team members may be added later. You will be asked to select from two tracks: Medtech Track: Teams will select Medtech Track if the subject technology is in the form of medical devices, diagnostics, medicines, vaccines, software, testing procedures and systems and is developed to solve a health/clinical problem and improve the quality of human life. Science and Engineering (S&E) Track: Teams will select S&E Track if the technology does not fit into the Medtech category.

The program supports four main research themes that are envisioned to advance the goal of ubiquitous co-robots: scalability, customizability, lowering barriers to entry, and societal impact, including human safety. Topics addressing scalability include how robots can collaborate effectively with orders of magnitude more humans or other robots than is handled by the current state of the art; how robots can perceive, plan, act, and learn in uncertain, real-world environments, especially in a distributed fashion; and how to facilitate large-scale, safe, robust and reliable operation of robots in complex environments. Customizability includes how to enable co-robots to adapt to specific different tasks, environments, or people, with minimal modification to hardware and software; how robots can personalize their interactions with people; and how robots can communicate naturally with humans, both verbally and non-verbally. Topics in lowering barriers to entry should focus on lowering the barriers for conducting fundamental robotics research and research on integrated robotics application.

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composingheterogeneous computing componentsand continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid stridesin machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred.

NSF-supported science and engineering research increasingly relies on cutting-edge infrastructure. With its Major Research Instrumentation (MRI) program and Major Multi-user Facilities (Major Facilities) projects, NSF supports infrastructure projects at the lower and higher ends of infrastructure scales across science and engineering research disciplines. The Mid-scale Research Infrastructure Big Idea is intended to provide NSF with an agile, Foundation-wide process to fund experimental research capabilities in the mid-scale range between the MRI and Major Facilities thresholds. NSF defines Research Infrastructure (RI) as any combination of facilities, equipment, instrumentation, or computational hardware or software, and the necessary human capital in support of the same. Major facilities and mid-scale projects are subsets of research infrastructure. The NSF Mid-scale Research Infrastructure-1 Program (Mid-scale RI-1) supports the design or implementation of unique and compelling RI projects. Mid-scale RI-1 implementation projects may include any combination of equipment, instrumentation, cyberinfrastructure, broadly used large-scale datasets, and the commissioning and/or personnel needed to successfully complete the project, or the design efforts intended to lead to eventual implementation of a mid-scale class project. Mid-scale RI-1 design projects will include the design efforts intended to lead to eventual implementation of a mid-scale class RI project. Mid-scale RI-1 projects should fill a research community-defined scientific need or enable a national research priority to be met. Mid-scale RI-projects should also enable US researchers to remain competitive in a global research environment and involve the training of a diverse workforce engaged in the design and implementation of STEM infrastructure.

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability, domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation.

Global research and development at Sony enables us to foster innovative ideas, which could ultimately lead to future technology advancements and company growth. In order to speed up and expand the creation of new ideas, we would like to partner with universities. This partnership will help cultivate advanced concepts and fertilize our own research and development. The Sony Faculty Innovation Award provides up to $100K in funds to conduct pioneering research in the areas of visualization; computer vision; machine learning; robotics; communications and networking; RF sensing; audio; speech and natural language processing; human computer interaction; mobility; system software; and LSI and hardware.

This solicitation addresses the overlapping objectives of theNational Space Weather Strategy and Action Plan(NSW-SAP) and theNational Strategic Computing Initiative(NSCI) Update through a pilot program. The goal of this pilot program is to transform development of predictive modeling of the coupled evolution of the magnetized solar atmosphere and the solar wind, and their interaction with the Earth's magnetosphere and upper atmosphere. This requires advancing our understanding of the necessary and sufficient requirements of model complexity, computational performance, and observational inputs. The pilot program is also expected to directly contribute to the long-term goal of creating space weather models with quantifiable predictive capability.