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Founded in 1996, the Marsha Rivkin Center for Ovarian Cancer Research provides funding for multiple efforts, including funding for innovative research pilot studies, scholar grants to up-and-coming investigators to encourage careers in ovarian cancer research, conducting public early detection screening for ovarian cancer, and producing nationally and internationally attended research symposia on ovarian cancer. To that end, the center is accepting applications for pilot study projects in ovarian cancer research. Through the center's Pilot Study Program, approximately ten one-year $75,000 grants will be awarded to support investigator-initiated projects in all areas of ovarian cancer research. Projects designed to analyze data from already funded clinical trials also will be considered. Priority will be given to proposals that are innovative, multidisciplinary, likely to lead to submission of grant applications for independently funded investigations, and have translational research potential.

The confluence of transistor scaling, increases in the number of architecture designs per process generation, the slowing of clock frequency growth, and recent success in research exploiting Thread Level Parallelism (TLP) and Data Level Parallelism (DLP) all point to an increasing opportunity for innovative microarchitecture techniques and methodologies in delivering performance growth in the future. The NSF/Intel Partnership on Foundational Microarchitecture Research will support transformative microarchitecture research targeting improvements in instructions per cycle (IPC). This solicitation seeks microarchitecture technique innovations beyond simplistic, incremental scaling of existing microarchitectural structures. Specifically, FoMR seeks to advance research that has the following characteristics: (1) high IPC techniques ranging from microarchitecture to code generation; (2) "microarchitecture turbo" techniques that marshal chip resources and system memory bandwidth to accelerate sequential or single-threaded programs; and (3) techniques to support efficient compiler code generation. Advances in these areas promise to provide significant performance improvements to continue the cadence promised by Moore's Law.

This mechanism supports basic research to increase knowledge/understanding through discovery and hypothesis generation, and should focus on providing basic fundamental knowledge that will inform and enable the future development of novel autonomous and/or robotic medical systems to care for wounded soldiers/patients through breakthrough, exploratory research. The objective is focused on addressing the following 1. Autonomous and Unmanned Medical Capability - Identify novel ideas, approaches and research towards the conceptualization of autonomous and unmanned technologies for next-generation, high-quality medical capabilities with limited or absent medical care personnel, or personnel with limited skills. Research novel concepts, plausible approaches and advanced concept designs using biologically inspired cognitive computing models, machine learning, artificial intelligence, soft robotic semi-autonomous/autonomous resuscitation concepts and advanced applications of information sciences among other innovative, exploratory research towards advancing the state-of-the-art in delivery of forward resuscitative care at the point of injury. 2. Medical Robotics Research - Identify novel ideas, approaches and research towards the conceptualization of medical robotics and real-time tele-presence capabilities exploring the limits of machine perception for tele-robotic semi-autonomous and autonomous trauma care within remote and dispersed geographic settings. This could include exploratory research in semi-autonomous robotic surgery to improve the safety profile and efficacy of tele-surgical procedures and outcomes using hard robotics in challenging situations (e.g., combat casualties on the multi-domain battlefield or mass casualty situations) and remote or austere geographic locations, among other innovative, exploratory research aims and novel concepts.

This Funding Opportunity Announcement (FOA) is associated with the Beau Biden Cancer MoonshotSM Initiative that is intended to accelerate cancer research. The purpose of this FOA is to establish Centers of collaborating investigators with the goal of identifying and advancing research opportunities for translating immunotherapy concepts for children and adolescents with cancer toward clinical applications. Specifically, this FOA targets the following area designated as a scientific priority by the Blue Ribbon Panel (BRP): Recommendation (B) that calls for the establishment of a pediatric immunotherapy translational science network. The network was envisioned by the BRP as focusing on identifying new targets for immunotherapies, developing new pediatric immunotherapy treatment approaches (e.g., cancer vaccines, cellular therapy, combinations of immunotherapy agents, and others), and defining the biological mechanisms by which pediatric tumors evade the immune system. The Pediatric Immunotherapy Discovery and Development Network (PI-DDN) Centers will address and implement these BRP recommendations.

This Funding Opportunity Announcement (FOA) is associated with the Beau Biden Cancer Moonshot InitiativeSM that is intended to accelerate cancer research. The purpose of this FOA is to promote research on developing the descriptive, inferential, and graphical statistical methods for data generated in clinical trials including Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE™) items. Specifically, this FOA targets the following area designated as a scientific priority by the Blue Ribbon Panel (BRP): Accelerate research that can identify approaches to monitor and manage patient-reported symptoms.

Research supported by the Division of Materials Research (DMR) focuses on advancing fundamental understanding of materials, materials discovery, design, synthesis, characterization, properties, and materials-related phenomena. DMR awards enable understanding of the electronic, atomic, and molecular structures, mechanisms, and processes that govern nanoscale to macroscale morphology and properties; manipulation and control of these properties; discovery of emerging phenomena of matter

Imaging systems are commonly used for conducting pavement evaluations. AASHTO Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection (AASHTO Designation: PP 68) addresses the collection of images. However, there are no widely accepted methods for measuring the characteristics of pavement surface images (such as 2-dimensional optical images and 3-dimensional surface elevation images). There are also no widely accepted AASHTO standard practices for the calibration, certification, and verification of such images. Research is needed to identify the characteristics of surface images that are essential for pavement evaluation and develop methods for measuring these characteristics, and also to develop recommended standard practices for implementing these methods. This information will help highway agencies better evaluate image data collection systems and improve the process of pavement condition evaluation. The objectives of this research are to (1) identify and develop methods for measuring the characteristics of surface images used for pavement evaluation and analysis; and (2) develop recommended standard practices for the calibration, certification, and verification of such images, for consideration and adoption by AASHT

The Ohio Affiliate of  Prevent Blindness is accepting applications for its 2018 Young Investigator Student Fellowship Awards for Female Scholars in Vision Research. The Fellowship Program is designed to provide support for outstanding female scientists committed to pursuing biomedical, behavioral or clinical research careers relevant to the mission of Prevent Blindness - to prevent blindness and preserve sight.  Grants will be awarded for the summer 2018 session.  Awards will range from $3000-$5000 depending upon the availability of funds. The deadline for receipt of applications is Feb. 15, 2018. Applicants must be post-baccalaureate students enrolled in a master's or doctorate program during the summer of 2018, female, citizens or permanent residents of the United States, and conducting their research with a recognized academic institution in the State of Ohio. Applications from diverse fields in the health sciences including, but not limited to ophthalmology, optometry, nursing, genetics, public health, nutrition, gerontology, and bioengineering, are appropriate to the goals of this fellowship award. The Ohio Affiliate of Prevent Blindness encourages fellowship applications which investigate public health issues related to the burden of eye-related health and safety topics.

This FOA solicits new theories, computational models, and statistical tools to derive understanding of brain function from complex neuroscience data. Proposed tools could include the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting dynamical features and patterns in complex brain data. It is expected that the tools developed under this FOA will be made widely available to the neuroscience research community for their use and modification. Investigative studies should be limited to validity testing of the tools being developed.

This funding opportunity announcement (FOA) seeks to develop and nurture a national innovation ecosystem that builds upon biomedical research to develop technologies, products, and services that benefit society. Toward meeting this objective, the I-Corps™ program is being offered. The I-Corps™ at NIH program is focused on educating researchers and technologists on how to translate technologies from the lab into the marketplace. Under this FOA, participating NIH and CDC Institutes and Centers will continue providing administrative supplement awards to currently-funded SBIR and STTR Phase I grantees. The program is designed to provide three-member project teams with access to instruction and mentoring in order to accelerate the translation of technologies currently being developed with NIH and Centers for Disease Control and Prevention (CDC) SBIR and STTR funding. It is anticipated that outcomes for the I-Corps™ teams participating in this program will include significantly refined commercialization plans and well-informed pivots in their overall commercialization strategies. Prospective applicants are strongly encouraged to contact NIH or CDC Scientific/Research staff for more information about the program before applying.

The International Research Experiences for Students (IRES) program supports development of globally-engaged U.S. science and engineering students capable of performing in an international research environment at the forefront of science and engineering.  The IRES program supports active research participation by students enrolled as undergraduates or graduate students in any of the areas of research funded by the National Science Foundation.  IRES projects involve students in meaningful ways in ongoing research programs or in research projects specifically designed for the IRES program. 

Run-off-road (ROR) traffic crashes account for almost one-third of the deaths and serious injuries each year on U.S. highways. The effective design of roadsides, including the placement of roadside safety devices, can reduce the frequency and/or severity of these crashes but requires an understanding of the nature and frequency of roadside encroachments. Unfortunately, the best quality encroachment data currently available were collected in the 1960s and 1970s. The age of these datasets means they are likely no longer representative of the current vehicle fleet or highway conditions. Further, each of these datasets has significant limitations, including specific exclusion of heavy vehicles and motorcycles and a very limited range of traffic volumes (i.e., less than 20,000 vehicles per day); such limitations have fostered much debate over the value of findings from these studies.

The "Campus Cyberinfrastructure - Infrastructure, Innovation and Engineering (CC*IIE)" program invests in improvements and re-engineering at the campus level to support a range of data transfers supporting computational science and computer networks and systems research. The program also supports Network Integration activities tied to achieving higher levels of performance, reliability and predictability for science applications and distributed research projects. CC*IIE awards will be made in six areas. Data Driven Networking Infrastructure for the Campus and Researcher awards will be supported at up to $500,000 total for up to 2 years. Network Design and Implementation for Small Institutions awards will be supported at up to $350,000 total for up to two years. Network Integration and Applied Innovation awards will be supported at up to $1,000,000 total for up to 2 years. Identity and Access Management Integration awards will be supported at up to $300,000 total for up to two years. Campus CI Engineer awards will be made at up to $400,000 total for up to 2 years. Regional Coordination and Partnership in Advanced Networking awards will be made at up to $150,000 for up to 2 years.

The primary objectives of the Reversible/Quantum Machine Learning and Simulation (RQMLS) AIE opportunity are (1) to explore the fundamental limits of reversible quantum annealers; (2) to quantitatively predict the computational utility of these systems for machine learning, simulation, and other important tasks; and (3) to design experimental tests for these predictions that can be carried out on newly fabricated small-scale reversible quantum annealers.

The Division of Molecular and Cellular Biosciences (MCB) has developed a new opportunity to enable researchers with a strong track record of prior accomplishment to pursue a new avenue of research or inquiry. This funding mechanism is designed to facilitate and promote a PI's ability to effective adopt empowering technologies that might not be readily accessible in the PI's current research environment or collaboration network. Transformative research likely spans disciplines and minimizing the practical barriers to doing so will strengthen research programs poised to make significant contributions. The award is intended to allow mid-career or later-stage researchers (Associate or Full Professor, or equivalent) to expand or make a transition in their research programs via a sabbatical leave or similar mechanism of professional development and then develop that research program in their own lab. This award will also enable the PI to acquire new scientific or technical expertise, facilitate the investigator's competitiveness, and potentially lead to transformational impacts in molecular and cellular bioscience.

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 Nation's advanced research cyberinfrastructure (CI) ecosystem catalyzes discovery and innovation across all areas of science and engineering (S&E) research and education. The increasingly complex and rapidly evolving S&E landscape requires an agile, integrated, robust, trustworthy, and sustainable CI ecosystem that will drive new thinking and transformative discoveries in all areas of research and education. The success of this vision depends on the ability of the research community to be able to easily and effectively access and use state-of-the-art research CI resources and services in a timely way. This, in turn, drives a set of requirements on the development, operation, and evolution of the CI ecosystem. First, research CI resources and services must be designed to leverage and drive innovations, and they must be user-centric and interoperable to enable the efficient, flexible end-to-end discovery pathways that are increasingly essential for the conduct of research. Second, the information, expertise, and services needed to maximally utilize the CI ecosystem must be disseminated broadly and concertedly to the research community. The NSF Cyberinfrastructure Centers of Excellence (CI CoE) Program aims to realize the above vision by supporting hubs of expertise and innovation targeting specific areas, aspects, or stakeholder communities of the research CI ecosystem.

I-Corps@Ohio is a statewide program developed to assist faculty, staff and students from Ohio universities, colleges and community colleges in validating the market potential of technologies and launching startup companies. I-Corps@Ohio is modeled after the National Science Foundation's (NSF) successful I-Corps (Innovation Corps) program, which has been proven to increase innovation, entrepreneurship, and industry collaboration. The I-Corps@Ohio program incorporates lean launch, customer discovery and business model innovation methodologies to assess technologies, enhance the business acumen of research faculty and students and expand their entrepreneurial network relationships. Two cohort tracks are offered in Science & Engineering and Medtech, with each designed to offer both common and subject matter specific content. The long-term objective of I-Corps@Ohio is to systematically build a steady and predictable pipeline of  high-quality, high-growth startups from technology developed at the State's colleges, universities, and research institutions, that contribute to economic development in Ohio.

ecoTech™ Grants were created to combat the notion that students needed to choose between "the screen" or "the green" and to encourage educators and students to explore the role technology can play in designing and implementing solutions to some of our most pressing environmental challenges. We believe that technology can present innovative ways to address environmental challenges - and that when dealing with digital natives, we do ourselves a disservice by asking them to unplug.

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.