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The goals of the Network for Computational Nanotechnology (NCN) are to: 1) accelerate the transformation of nanoscience to nanotechnology through the integration of simulation with experimentation; 2) engage an ever-larger and more diverse cyber community sharing novel, high-quality nanoscale computation and simulation research and educational resources; 3) develop open-access, open-source software to stimulate data sharing; and 4) inspire and educate the next-generation workforce. The NCN consists of a stand-alone Cyber Platform, which provides computation, simulation, and education services to over 330,000 researchers, educators, students, and industry members of the nanoscience and engineering community annually worldwide; and Nodes, which develop compelling new computational and simulation tools to disseminate through Cyber Platform (nanoHUB.org) and cultivate communities of users in emerging areas of nanoscale science and engineering.

The goals of the Network for Computational Nanotechnology (NCN) are to: 1) accelerate the transformation of nanoscience to nanotechnology through the integration of simulation with experimentation; 2) engage an ever-larger and more diverse cyber community sharing novel, high-quality nanoscale computation and simulation research and educational resources; 3) develop open-access, open-source software to stimulate data sharing; and 4) inspire and educate the next-generation workforce. The NCN consists of a stand-alone Cyber Platform, which provides computation, simulation, and education services to over 330,000 researchers, educators, students, and industry members of the nanoscience and engineering community annually worldwide; and Nodes, which develop compelling new computational and simulation tools to disseminate through Cyber Platform (nanoHUB.org) and cultivate communities of users in emerging areas of nanoscale science and engineering.

The Solar and Planetary Research Grants (SPG) Program provides individual investigator and collaborative research grants for observational, theoretical, laboratory, and archival data studies in the science of our solar system and extrasolar planetary systems. Proposals for projects and tools that enable and enhance research in those areas may also be submitted.

RII Track-2 FEC builds interjurisdictional collaborative teams of EPSCoR investigators in scientific focus areas consistent with NSF priorities. Projects are investigator-driven and must include researchers from at least two RII- eligible jurisdictions with complementary expertise and resources necessary to tackle those projects, which neither party could address as well or rapidly alone. The Science, Technology, Engineering, and Mathematics (STEM) research and education activities should seek to broaden participation through the strategic inclusion and integration of different types of individuals, institutions, and sectors throughout the project. Proposals must describe a comprehensive and integrated vision to drive discovery and build sustainable STEM capacity that exemplifies diversity of all types (individual, institutional, geographic, and disciplinary). The development of diverse early-career faculty is a critical component of this sustainable STEM capacity. For FY 2019, RII Track-2 FEC proposals are invited on a single topic: "Harnessing the Data Revolution to solve problems of national importance."

The NSF Research Traineeship (NRT) program is designed to encourage the development and implementation of bold, new, and potentially transformative models for science, technology, engineering and mathematics (STEM) graduate education training. The NRT program seeks proposals that explore ways for graduate students in research-based master's and doctoral degree programs to develop the skills, knowledge, and competencies needed to pursue a range of STEM careers. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas, through the use of a comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs. Proposals are requested in any interdisciplinary or convergent research theme of national priority, with special emphasis on the research areas in NSF's 10 Big Ideas. The NSF research Big Ideas are Harnessing the Data Revolution (HDR), The Future of Work at the Human-Technology Frontier (FW-HTF), Navigating the New Arctic (NNA), Windows on the Universe: The Era of Multi-Messenger Astrophysics (WoU), The Quantum Leap: Leading the Next Quantum Revolution (QL), and Understanding the Rules of Life: Predicting Phenotype (URoL). The NRT program addresses workforce development, emphasizing broad participation, and institutional capacity building needs in graduate education.

CubeSat constellations and swarms have been identified as a new paradigm for space-based measurements to address high-priority science questions in multiple disciplines. However, the full potential of CubeSat constellations and swarms for scientific studies has not yet been realized because of: i) the limitations of some of the existing key CubeSat technology, ii) knowledge gaps in the design and optimization of CubeSat technology for swarms and constellations, and iii) the increasing cost of more sophisticated CubeSat technology. The technology challenges include high bandwidth communications in CubeSat-to-CubeSat and CubeSat-to-ground scenarios, circuits and sensors miniaturization, on-board signal processing, and power generation. The vision of a satellite mission consisting of 10-100 CubeSats will require focused investment and development in a myriad of CubeSat-related technologies to build a cost-effective constellation or swarm of CubeSats. This will require transformative approaches for designing and building CubeSat subsystems and sensors, and innovative production approaches that will reduce the cost of implementing large-scale constellation missions. Spectrum allocations for data transmission and possible electromagnetic interference between or within constellations of CubeSats are issues that also will need to be considered.

The NSF Research Traineeship (NRT) program is designed to encourage the development and implementation of bold, new, and potentially transformative models for STEM graduate education training. The NRT program seeks proposals that explore ways for graduate students in research-based master's and doctoral degree programs to develop the skills, knowledge, and competencies needed to pursue a range of STEM careers. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the use of a comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs. For FY2018, proposals are requested in any interdisciplinary research theme of national priority, with special emphasis on two high priority areas: (1) Harnessing the Data Revolution (HDR) and (2) Innovations at the Nexus of Food, Energy, and Water Systems (INFEWS). HDR is expected to continue as a priority research area for FY2019 and FY2020 competitions, along with a new priority area to be announced in 2018. The NRT program addresses workforce development, emphasizing broad participation, and institutional capacity building needs in graduate education. Strategic collaborations with the private sector, non-governmental organizations (NGOs), government agencies, national laboratories, field stations, teaching and learning centers, informal science centers, and academic partners are encouraged. NRT especially welcomes proposals that will pair well with the efforts of NSF INCLUDES to develop STEM talent from all sectors and groups in our society (https://www.nsf.gov/news/special_reports/nsfincludes/index.jsp). Collaborations are encouraged between NRT proposals and existing NSF INCLUDES projects, provided the collaboration strengthens both projects.

CubeSat constellations and swarms have been identified as a new paradigm for space-based measurements to address high-priority science questions in multiple disciplines. However, the full potential of CubeSat constellations and swarms for scientific studies has not yet been realized because of: i) the limitations of some of the existing key CubeSat technology, ii) knowledge gaps in the design and optimization of CubeSat technology for swarms and constellations, and iii) the increasing cost of more sophisticated CubeSat technology. The technology challenges include high bandwidth communications in CubeSat-to-CubeSat and CubeSat-to-ground scenarios, circuits and sensors miniaturization, on-board signal processing, and power generation. The vision of a satellite mission consisting of 10-100 CubeSats will require focused investment and development in a myriad of CubeSat-related technologies to build a cost-effective constellation or swarm of CubeSats. This will require transformative approaches for designing and building CubeSat subsystems and sensors, and innovative production approaches that will reduce the cost of implementing large-scale constellation missions. Spectrum allocations for data transmission and possible electromagnetic interference between or within constellations of CubeSats are issues that also will need to be considered.

NSWC Crane is interested in funding research to inform future science and technology investments via data mapping and information visualization techniques. Specifically, advancements beyond traditional scientometrics are desired for visualizing, understanding, and predicting naval research and development investments that yield transitioned technologies via methods that make sense of the dynamic collaborative network of academia, industry, and government partners.

NSWC Crane is interested in funding research to inform future science and technology investments via data mapping and information visualization techniques. Specifically, advancements beyond traditional scientometrics are desired for visualizing, understanding, and predicting naval research and development investments that yield transitioned technologies via methods that make sense of the dynamic collaborative network of academia, industry, and government partners.

wards will be made as grants, cooperative agreements, contracts, and inter- or intra-agency transfers, depending on the nature of the work proposed, the proposing organization, and/or program requirements. The typical period of performance for an award is three years, but some programs may allow up to five years and others specify shorter periods. Organizations of every type, domestic and foreign, Government and private, for profit and not-for-profit, may submit proposals without restriction on teaming arrangements. Note that it is NASA policy that all research involving non-U.S. organizations will be conducted on the basis of no exchange of funds. Awards range from under $100K per year for focused, limited efforts (e.g., data analysis) to more than $1M per year for extensive activities (e.g., development of hardware for science experiments and/or flight). The funds available for awards in each program element offered in ROSES-2018 range from less than one to several million dollars, which allows for selection from a few to as many as several dozen proposals, depending upon the program objectives and the submission of proposals of merit. Electronic submission of proposals is required by the respective due dates for each program element and must be submitted by an authorized official of the proposing organization.

The Operations Engineering (OE) program supports fundamental research on advanced analytical methods for improving operations in complex decision-driven environments. Analytical methods include, but are not limited to, deterministic and stochastic modeling, optimization, decision and risk analysis, data science, and simulation. Methodological research is highly encouraged but must be motivated by problems that have potential for high impact in engineering applications. Application domains of particular interest to the program arise in commercial enterprises (e.g., production/manufacturing systems and distribution of goods, delivery of services), the public sector/government (e.g., public safety and security), and public/private partnerships (e.g., health care, environment and energy). The program also welcomes operations research in new and emerging domains and addressing systemic societal or technological problems. The OE program particularly values cross-disciplinary proposals that leverage application-specific expertise with strong quantitative analysis in a decision-making context. Proposals for methodological research that are not strongly motivated by high-potential engineering applications are not appropriate for this program.

The Electrical, Communications and Cyber Systems Division (ECCS) supports enabling and transformative engineering research at the nano, micro, and macro scales that fuels progress in engineering system applications with high societal impact. This includes fundamental engineering research underlying advanced devices and components and their seamless penetration in power, controls, networking, communications or cyber systems. The research is envisioned to be empowered by cutting-edge computation, synthesis, evaluation, and analysis technologies and is to result in significant impact for a variety of application domains in healthcare, homeland security, disaster mitigation, telecommunications, energy, environment, transportation, manufacturing, and other systems-related areas. ECCS also supports new and emerging research areas encompassing 5G and Beyond Spectrum and Wireless Technologies, Quantum Information Science, Artificial Intelligence, Machine Learning, and Big Data.

The Electrical, Communications and Cyber Systems Division (ECCS) supports enabling and transformative engineering research at the nano, micro, and macro scales that fuels progress in engineering system applications with high societal impact. This includes fundamental engineering research underlying advanced devices and components and their seamless penetration in power, controls, networking, communications or cyber systems. The research is envisioned to be empowered by cutting-edge computation, synthesis, evaluation, and analysis technologies and is to result in significant impact for a variety of application domains in healthcare, homeland security, disaster mitigation, telecommunications, energy, environment, transportation, manufacturing, and other systems-related areas. ECCS also supports new and emerging research areas encompassing 5G and Beyond Spectrum and Wireless Technologies, Quantum Information Science, Artificial Intelligence, Machine Learning, and Big Data.

The Electrical, Communications and Cyber Systems Division (ECCS) supports enabling and transformative engineering research at the nano, micro, and macro scales that fuels progress in engineering system applications with high societal impact. This includes fundamental engineering research underlying advanced devices and components and their seamless penetration in power, controls, networking, communications or cyber systems. The research is envisioned to be empowered by cutting-edge computation, synthesis, evaluation, and analysis technologies and is to result in significant impact for a variety of application domains in healthcare, homeland security, disaster mitigation, telecommunications, energy, environment, transportation, manufacturing, and other systems-related areas. ECCS also supports new and emerging research areas encompassing 5G and Beyond Spectrum and Wireless Technologies, Quantum Information Science, Artificial Intelligence, Machine Learning, and Big Data.

Growing Convergence Research (GCR) at the National Science Foundation was identified as one of 10 Big Ideas. Convergence research is a means for solving vexing research problems, in particular, complex problems focusing on societal needs. It entails integrating knowledge, methods, and expertise from different disciplines and forming novel frameworks to catalyze scientific discovery and innovation. GCR identifies Convergence Research as having two primary characteristics: Research driven by a specific and compelling problem. Convergence Research is generally inspired by the need to address a specific challenge or opportunity, whether it arises from deep scientific questions or pressing societal needs. Deep integration across disciplines. As experts from different disciplines pursue common research challenges, their knowledge, theories, methods, data, research communities and languages become increasingly intermingled or integrated. New frameworks, paradigms or even disciplines can form sustained interactions across multiple communities. A distinct characteristic of convergence research, in contrast to other forms of multidisciplinary research, is that from the inception, the convergence paradigm intentionally brings together intellectually diverse researchers and stakeholders to frame the research questions, develop effective ways of communicating across disciplines and sectors, adopt common frameworks for their solution, and, when appropriate, develop a new scientific vocabulary. Research teams practicing convergence aim at developing sustainable relationships that may not only create solutions to the problem that engendered the collaboration, but also develop novel ways of framing related research questions and open new research vistas.

Next-generation wireless networks, utilizing a wide swath of wireless spectrum and an array of novel technologies in the wired and wireless domains, are on the cusp of unleashing a broadband revolution with promised peak bit rates of tens of gigabits per second and latencies of less than a millisecond. Such innovations will make possible a new set of applications such as autonomous vehicles, industrial robotics, tactile Internet applications, virtual and augmented reality, and dense Internet of Things (IoT) deployments. A key requirement of these applications is fast information response time that is invariant as a function of the bandwidth demanded, users/devices supported, and data generated, of which low-latency wireless access time is only one component. Intrinsic security, seamless mobility, scalable content caching, and discovery/distribution services are also essential for such applications. This solicitation seeks unique data network architectures featuring an information plane using an Information-Centric Networking (ICN) approach and addressing discovery, movement, delivery, management, and protection of information within a network, along with the abstraction of an underlying communication plane creating opportunities for new efficiencies and optimizations across communications technologies that could also address latency and scale requirements.

The need to process large data sets arising in many practical problems require real-time learning from data streams makes high-performance hardware necessary, and yet the very nature of these problems, along with currently known algorithms for addressing them, imposes significant hardware challenges. Current versions of deep-learning algorithms operate by using millions of parameters whose optimal values need to be determined for good performance in real time on high-performance hardware. Conversely, the availability of fast hardware implementations can enable fuller use of Bayesian techniques, attractive for their ability to quantify prediction uncertainty and thus give estimates of reliability and prediction breakdown. The abilities of ML systems to self-assess for reliability and predict their own breakdowns (and also recover without significant ill effects) constitute critical areas for algorithm development as autonomous systems become widely deployed in both decision support and embodied AI agents. Only with attention to these challenges can we construct systems that are robust when they encounter novel situations or degradation and failure of sensors.

The Office of Naval Research (ONR) is interested in receiving proposals for efforts that will advance and demonstrate science and technology for the next generation electronics and devices under the following focus area: Electronics technology enablers for wideband Simultaneous Transmit and Receive (STAR) capabilities Background The need for concurrent military antenna operations across wide spectral ranges in heavily congested electromagnetic environments continues to expand. Steady advances in RF and mixed-signal electronics technology continue to fuel increased system performance capabilities through the use of higher operating frequencies and broader bandwidths. Higher resolution for active sensors/imagers, higher data rate terrestrial and satellite communications links, and more effective electronic warfare (EW) and Information Operations (IO) are a few of the advances that high-speed electronics continues to enable. Many solid state device technologies from Silicon to Gallium Nitride, Niobium to Photonics, are contributing to these military system advances. Significant electronic challenges arise when these EW/IO, communications and radar systems are required to operate concurrently, with both transmit and receive functionality utilizing either a single aperture or multiple apertures. The concurrency problem is exasperated by the desire of each system to project more power from smaller overall platform footprints in order to maximize performance and minimize signature. While electronics technology advances have led to significant military antenna system performance gains, the ability to operate these systems concurrently in a STAR configuration without severe performance degradation continues to be severely lacking. The need for improved wideband STAR enabling electronics technology is the primary focus of this BAA.

Computational approaches play a central role in understanding and advancing sustainability.  CyberSEES supports research on all sustainability topics that depend on advances in computational areas including optimization, modeling, simulation, prediction, and inference; large-scale data management and analytics; advanced sensing techniques; human computer interaction and social computing; infrastructure design, control and management; and intelligent systems and decision-making. Additionally, the widespread, intensive use of computing technologies also introduces sustainability challenges and motivates new approaches across the lifecycle of technology design and use.