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The goals of the Resilient Interdependent Infrastructure Processes and Systems (RIPS) solicitation are (1) to foster an interdisciplinary research community that discovers new knowledge for the design and operation of infrastructures as processes and services  (2) to enhance the understanding and design of interdependent critical infrastructure systems (ICIs) and processes that provide essential goods and services despite disruptions and failures from any cause, natural, technological, or malicious, and (3) to create the knowledge for innovation in ICIs to advance society with new goods and services. The objectives of this solicitation are: Create theoretical frameworks and multidisciplinary computational models of interdependent infrastructure systems, processes and services, capable of analytical prediction of complex behaviors, in response to system and policy changes. Synthesize new approaches to increase resilience, interoperations, performance, and readiness in ICIs. Understand organizational, social, psychological, legal, political and economic obstacles to improving ICI's, and identifying strategies for overcoming those obstacles. The RIPS solicitation seeks proposals with transformative ideas that will ensure ICIs services are effective, efficient, dependable, adaptable, resilient, safe, and secure.  Successful proposals are expected to study multiple infrastructures focusing on them as interdependent systems that deliver services, enabling a new interdisciplinary paradigm in infrastructure research.  Proposals that do not broadly integrate across the cyber-physical, engineering and social, behavioral and economic (SBE) sciences may be returned without review. 

The Division of Molecular and Cellular Biosciences (MCB) supports quantitative, predictive, and theory-driven fundamental research and related activities designed to promote understanding of complex living systems at the molecular, subcellular, and cellular levels. MCB is soliciting proposals for hypothesis-driven and discovery research and related activities in four core clusters: Molecular Biophysics Cellular Dynamics and Function Genetic Mechanisms Systems and Synthetic Biology MCB gives high priority to research projects that use theory, methods, and technologies from physical sciences, mathematics, computational sciences, and engineering to address major biological questions.  Research supported by MCB uses a range of experimental approaches--including in vivo, in vitro and in silico strategies--and a broad spectrum of model and non-model organisms, especially microbes and plants. Typical research supported by MCB integrates theory and experimentation.  Projects that address the emerging areas of multi-scale integration, molecular and cellular evolution, quantitative prediction of phenome from genomic information, and development of methods and resources are particularly welcome.

The Communications, Circuits, and Sensing-Systems (CCSS) program is intended to spur visionary systems-oriented activities in collaborative, multidisciplinary, and integrative research. CCSS supports systems research in hardware, signal processing techniques, and architectures to enable the next generation of cyber-physical systems (CPS) that leverage computation, communication, and algorithms integrated with physical domains. CCSS offers new challenges at all levels of systems integration to address future societal needs. CCSS supports innovative research and integrated educational activities in micro- and nano-systems, communications systems, and cyber-physical systems. The goal is to design, develop, and implement new complex and hybrid systems at all scales, including nano, micro, and macro, that lead to innovative engineering principles and solutions for a variety of application domains including, but not limited to, healthcare, medicine, environmental monitoring, communications, disaster mitigation, homeland security, transportation, manufacturing, energy, and smart buildings. CCSS also supports integration technologies at both intra-and inter-chip levels, new and advanced radio frequency (RF), millimeter wave and optical wireless and hybrid communications systems architectures, and sensing and imaging at terahertz (THz) frequencies.

CMMT supports theoretical and computational materials research in the topical areas represented in DMR's Topical Materials Research Programs (these are also variously known as Individual Investigator Award (IIA) Programs, or Core Programs, or Disciplinary Programs), which include: Condensed Matter Physics (CMP), Biomaterials (BMAT), Ceramics (CER), Electronic and Photonic Materials (EPM), Metals and Metallic Nanostructures (MMN), Polymers (POL), and Solid State and Materials Chemistry (SSMC). The CMMT program supports fundamental research that advances conceptual understanding of hard and soft materials, and materials-related phenomena; the development of associated analytical, computational, and data-centric techniques; and predictive materials-specific theory, simulation, and modeling for materials research.Research may encompass the advance of new paradigms in materials research, including emerging data-centric approaches utilizing data-analytics or machine learning. Computational efforts span from the level of workstations to advanced and high-performance scientific computing. Emphasis is on approaches that begin at the smallest appropriate length scale, such as electronic, atomic, molecular, nano-, micro-, and mesoscale, required to yield fundamental insight into material properties, processes, and behavior, to predict new materials and states of matter, and to reveal new materials phenomena. Approaches that span multiple scales of length and time may be required to advance fundamental understanding of materials properties and phenomena, particularly for polymeric materials and soft matter.

DMREF is the primary program by which NSF participates in the Materials Genome Initiative (MGI) for Global Competitiveness. MGI recognizes the importance of materials science and engineering to the well-being and advancement of society and aims to "deploy advanced materials at least twice as fast as possible today, at a fraction of the cost." MGI integrates materials discovery, development, property optimization, and systems design with a shared computational framework. This framework facilitates collaboration and coordination of research activities, analytical tools, experimental results, and critical evaluation in pursuit of the MGI goals. Consistent with theMGI Strategic Plan, DMREFhighlights four sets of goals: · Leading a culture shift in materials science and engineering research to encourage and facilitate an integrated team approach; · Integrating experimentation, computation, and theory and equipping the materials scienceand engineering communities with advanced tools and techniques; · Making digital data accessible, findable,and useful to the community; and · Creating a world-class materials science and engineering workforce that is trained for careers in academia or industry. Accordingly, DMREF will support activities that significantly accelerate materials discovery and/or development by building the fundamental knowledge base needed to design and make materials and/or devices with specific and desired functions or properties.

DMREF is the primary program by which NSF participates in the Materials Genome Initiative (MGI) for Global Competitiveness. MGI recognizes the importance of materials science and engineering to the well-being and advancement of society and aims to "deploy advanced materials at least twice as fast as possible today, at a fraction of the cost." MGI integrates materials discovery, development, property optimization, and systems design with a shared computational framework. This framework facilitates collaboration and coordination of research activities, analytical tools, experimental results, and critical evaluation in pursuit of the MGI goals. The MGI Strategic Plan highlights four sets of goals: -Leading a culture shift in materials science research to encourage and facilitate an integrated team approach; -Integrating experimentation, computation, and theory and equipping the materials community with advanced tools and techniques; -Making digital data accessible; and -Creating a world-class materials science and engineering workforce that is trained for careers in academia or industry.

DMREF is the primary program by which NSF participates in the Materials Genome Initiative (MGI) for Global Competitiveness. MGI recognizes the importance of materials science and engineering to the well-being and advancement of society and aims to "deploy advanced materials at least twice as fast as possible today, at a fraction of the cost." MGI integrates materials discovery, development, property optimization, and systems design with a shared computational framework. This framework facilitates collaboration and coordination of research activities, analytical tools, experimental results, and critical evaluation in pursuit of the MGI goals. The MGI Strategic Plan highlights four sets of goals: -Leading a culture shift in materials science research to encourage and facilitate an integrated team approach; -Integrating experimentation, computation, and theory and equipping the materials community with advanced tools and techniques; -Making digital data accessible; and -Creating a world-class materials science and engineering workforce that is trained for careers in academia or industry.

The NSF ADVANCE program contributes to the National Science Foundation's goal of a more diverse and capable science and engineering workforce.1 In this solicitation, the NSF ADVANCE program seeks to build on prior NSF ADVANCE work and other research and literature concerning gender, racial, and ethnic equity. The NSF ADVANCE program goal is to broaden the implementation of evidence-based systemic change strategies that promote equity for STEM2 faculty in academic workplaces and the academic profession. The NSF ADVANCE program provides grants to enhance the systemic factors that support equity and inclusion and to mitigate the systemic factors that create inequities in the academic profession and workplaces. Systemic (or organizational) inequities may exist in areas such as policy and practice as well as in organizational culture and climate. For example, practices in academic departments that result in the inequitable allocation of service or teaching assignments may impede research productivity, delay advancement, and create a culture of differential treatment and rewards. Similarly, policies and procedures that do not mitigate implicit bias in hiring, tenure, and promotion decisions could lead to women and racial and ethnic minorities being evaluated less favorably, perpetuating historical under-participation in STEM academic careers and contributing to an academic climate that is not inclusive.

The goal of the NSF Smart and Connected Communities (S&CC) program solicitation is to accelerate the creation of the scientific and engineering foundations that will enable smart and connected communities to bring about new levels of economic opportunity and growth, safety and security, health and wellness, and overall quality of life. This goal will be achieved through integrative research projects that pair advances in technological and social dimensions with meaningful community engagement. For the purposes of this solicitation, communities are defined as having geographically-delineated boundaries-such as towns, cities, counties, neighborhoods, community districts, rural areas, and tribal regions-consisting of various populations, with the structure and ability to engage in meaningful ways with proposed research activities. A "smart and connected community" is, in turn, a community that synergistically integrates intelligent technologies with the natural and built environments, including infrastructure, to improve the social, economic, and environmental well-being of those who live, work, or travel within it. A proposal for an S&CC Integrative Research Grants must include the following: Integrative research that addresses the technological and social dimensions of smart and connected communities; Meaningful community engagement that integrates community stakeholders within the project; A management plan that summarizes how the project will be managed across disciplines, institutions, and community entities; and An evaluation plan for assessing short-, medium-, and long-term impacts of the proposed activities.

The mission of the Biomedical Engineering (BME) program is to provide opportunities to develop novel ideas into discovery-level and transformative projects that integrate engineering and life science principles in solving biomedical problems that serve humanity in the long-term.  The Biomedical Engineering (BME) program supports fundamental research in the following BME themes: Neural engineering (brain science, computational neuroscience, brain-computer interface, neurotech, cognitive engineering) Cellular biomechanics (motion, deformation, and forces in biological systems; how mechanical forces alter cell growth, differentiation, movement, signal transduction, transport, cell adhesion, cell cytoskeleton dynamics, cell-cell and cell-ECM interactions; genetically engineered stem cell differentiation with long-term impact in tissue repair and regenerative medicine) The BME projects must be at the interface of engineering and life sciences, and advance both engineering and life sciences.  The projects should focus on high impact transforming methods and technologies. The project should include methods, models and tools of understanding and controlling of living systems; fundamental improvements in deriving information from cells, tissues, organs, and organ systems; new approaches to the design of structures and materials for eventual medical use in the long-term; and new novel methods of reducing health care costs through new technologies. The projects should emphasize the advancement of fundamental engineering knowledge, possibly leading to the development of new methods and technologies in the long-term; and highlight multi-disciplinary nature, integrating engineering and the sciences. The long-term impact of the projects can be related to disease diagnosis and/or treatment, improved health care delivery, or product development.

The Department of Energy's (DOE) Office of Nuclear Energy (NE) conducts crosscutting nuclear energy research and development (R&D) and associated infrastructure support activities to develop innovative technologies that offer the promise of dramatically improved performance for advanced reactors and fuel cycle concepts while maximizing the impact of DOE resources. NE strives to promote integrated and collaborative research conducted by national laboratory, university, industry, and international partners under the direction of NE's programs. NE funds research activities through both competitive and direct mechanisms, as required to best meet the needs of NE. This approach ensures a balanced R&D portfolio and encourages new nuclear power deployment with creative solutions to the universe of nuclear energy challenges. This FOA addresses the competitive portion of NE's R&D portfolio as executed through the Nuclear Energy University Programs (NEUP), Nuclear Energy Enabling Technologies Crosscutting Technology Development (NEET CTD), and Advanced Test Reactor National Scientific User Facility (ATR NSUF). NEUP utilizes up to 20 percent of funds appropriated to NE's R&D program for university-based infrastructure support and R&D in key NE program-related areas: Fuel Cycle Research and Development (FC R&D), Reactor Concepts Research, Development and Demonstration (RC RD&D), and Nuclear Energy Advanced Modeling and Simulation (NEAMS). NEET CTD supports national laboratory-, university- and industry-led crosscutting research. By establishing the NSUF in 2007, DOE-NE opened up the world of material test reactors, beam lines, and post-irradiation examination facilities to researchers from U.S. universities, industry and national laboratories by granting no-cost access to world-class nuclear research facilities. In addition to the consolidation of the NSUF Call for Applications (CFA) for access to capabilities, NEUP or NEET CTD projects requiring irradiation testing and/

The International Research Network Connections (IRNC) program supports high-performance network connectivity required by international science and engineering research and education collaborations involving the NSF research community. NSF expects to make 1-2 awards to link U.S. research networks with peer networks in Europe and Africa and leverage existing international network connectivity. High-performance network connections funded by this program are intended to support science and engineering research and education applications, and preference will be given to solutions that provide the best economy of scale and demonstrate the ability to support the largest communities of interest with the broadest services. Funded projects will assist the U.S. research and education community by enabling state-of-the-art international network services and access to increased collaboration and data services. Through extended international network connections, additional research and production network services will be enabled, complementing those currently offered or planned by domestic research networks.

The International Research Network Connections (IRNC) program supports high-performance network connectivity required by international science and engineering research and education collaborations involving the NSF research community. NSF expects to make 1-2 awards to link U.S. research networks with peer networks in Europe and Africa and leverage existing international network connectivity. High-performance network connections funded by this program are intended to support science and engineering research and education applications, and preference will be given to solutions that provide the best economy of scale and demonstrate the ability to support the largest communities of interest with the broadest services. Funded projects will assist the U.S. research and education community by enabling state-of-the-art international network services and access to increased collaboration and data services. Through extended international network connections, additional research and production network services will be enabled, complementing those currently offered or planned by domestic research networks.

This program seeks to prepare, nurture, and grow the national scientific research workforce for creating, utilizing, and supporting advanced cyberinfrastructure (CI) to enable and potentially transform fundamental science and engineering research and contribute to the Nation's overall economic competitiveness and security. The goals of this solicitation are to (i) ensure broad adoption of CI tools, methods, and resources by the research community in order to catalyze major research advances and to enhance researchers' abilities to lead the development of new CI; and (ii) integrate core literacy and discipline-appropriate advanced skills in advanced CI as well as computational and data-driven science and engineering into the Nation's educational curriculum/instructional material fabric spanning undergraduate and graduate courses for advancing fundamental research. Pilot and Implementation projects may target one or both of the solicitation goals, while Large-scale Project Conceptualization projects must address both goals. For the purpose of this solicitation, advanced CI is broadly defined as the set of resources, tools, methods, and services for advanced computation, large-scale data handling and analytics, and networking and security for large-scale systems that collectively enable potentially transformative fundamental research.

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. This solicitation describes an Ideas Lab focused onCubeSat Innovations to push the envelope of space-based research capabilities by simultaneously developing enabling technologies in several domains, including propulsion systems, sensor design, electronic circuits, antennas, satellite-to-ground and satellite-to-satellite communications and wireless networking, and power management. The vision of this Ideas Lab is to support research and engineering technology development efforts that will lead to new science missions in geospace and atmospheric sciences using self-organizing CubeSat constellations/swarms.

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.

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.

The Acquisition Research Program (ARP) (www.acquisitionresearch.net) conducts and supports research in academic disciplines that bear on public procurement policy and management. These include economics, finance, financial management, information systems, organization theory, operations management, human resources management, risk management, and marketing, as well as the traditional public procurement areas such as contracting, program/project management, logistics, test and evaluation and systems engineering management. The ARP is interested in innovative proposals that will provide unclassified and non-proprietary findings suitable for publication in open scholarly literature. Studies of government processes, systems, or policies should also expand the body of knowledge and theory of processes, systems, or policies outside the government. The following research areas are of special interest: Leading-edge techniques in data collection, management, visual analytics and decision-making; Robust risk modeling techniques; Performance metrics and methodologies; Collaboration and cross-functional teams; and, Model-Based Acquisition. Offerors bear prime responsibility for the design, management, direction and conduct of research. Researchers should exercise judgment and original thought toward attaining the goals within broad parameters of the research areas proposed and the resources provided. Offerors are encouraged to be creative in the selection of the technical and management processes and approaches and consider the greatest and broadest impact possible.

The EBRC (Energetics Basic Research Center) is a basic research program initiated by the Combat Capabilities Development Command/Army Research Laboratory/ARO. It focuses on areas of strategic importance to U.S. national security. It seeks to increase the Army's intellectual capital in energetic materials (EM) and improve its ability to address future challenges. EBRC brings together universities, research institutions, companies, and individual scholars and supports multidisciplinary and cross-institutional projects addressing specific topic areas determined by the Department of the Army (DA). The EBRC aims to promote research in specific areas of EMs and to promote a candid and constructive relationship between DA and the energetics research community. The future Army is projected to be unable to achieve dominance in range and lethality due to inadequate energetic formulations and form factor limitations associated with current weapon systems. Basic research generates new knowledge that may be exploited to develop and deliver new materials and technologies that contribute to enhanced lethal effects at the system level as well as increased range and a smaller payload. These, in turn, enable space for larger, mission-critical systems, and shorter time-to-target ensuring Army battlefield dominance in Multi-Domain Operations. Army research must encompass new ways to expedite the discovery, design, and scale-up of new materials and concepts which when integrated into newly designed weapons components (e.g. additively manufactured high strength steels with pre-formed fragmentation patterns, and structural reactive materials) developed at ARL and across the Army and DoD communities, will deliver decisive weapons overmatch.

This Funding Opportunity Announcement (FOA) intends to accelerate the integration and use of scalable technologies and tools to enhance and reinvigorate brain cell census research, including the development of technology platforms and/or resources that will enable a swift and comprehensive survey of brain cell types and circuits. Of particular interest are those that will (a) improve technology and resource platforms to remove limitations and bottlenecks in the current pipeline of brain cell census data generation; (b) integrate experimental and computational methods to enhance capabilities of cell census data generation and analysis and to reduce barriers to hypothesis-driven research; (c) generate a substantial amount of spatiotemporal cell census data and/or resources to demonstrate the utility of the improved technology and resource platforms; and (d) conduct comparative studies by using proper criteria to evaluate and benchmark quality of biospecimen, performance of cell census tools/technologies, and effectiveness of computational approaches. The projects funded under this FOA will align with the overarching goals of the BRAIN Initiative Cell Census Network (BICCN) and are expected to generate a substantial amount of cell census data using the proposed technologies or via collaboration with the BICCN.

The Smart and Autonomous Systems (S&AS) program focuses on Intelligent Physical Systems (IPS) that are capable of robust, long-term autonomy requiring minimal or no human operator intervention in the face of uncertain, unanticipated, and dynamically changing situations. IPS are systems that combine perception, cognition, communication, and actuation to operate in the physical world. Examples include, but are not limited to, robotic platforms, self-driving vehicles, underwater exploration vehicles, and smart grids. Most current IPS operate in pre-programmed ways and in a limited variety of contexts. They are largely incapable of handling novel situations, or of even understanding when they are outside their areas of expertise. To achieve robust, long-term autonomy, however, future IPS need to be aware of their capabilities and limitations and to adapt their behaviors to compensate for limitations and/or changing conditions. To foster such intelligent systems, the S&AS program supports research in four main aspects of IPS: cognizant, taskable, adaptive, and ethical. Cognizant IPS exhibit high-level awareness of their own capabilities and limitations, anticipating potential failures and re-planning accordingly. Taskable IPS can interpret high-level, possibly vague, instructions, planning out and executing concrete actions that are dependent on the particular context in which the system is operating. Adaptive IPS can change their behaviors over time, learning from their own experiences and those of other entities, such as other IPS or humans, and from instruction or observation. Ethical IPS should adhere to a system of societal and legal rules, taking those rules into account when making decisions. Each of these research areas requires the IPS to be knowledge-rich, employing a variety of representation and reasoning mechanisms, such as semantic, probabilistic, commonsense, and meta-reasoning.

The goal of the interagency Smart and Connected Health (SCH): Connecting Data, People and Systems program is to accelerate the development and integration of innovative computer and information science and engineering approaches to support the transformation of health and medicine. Approaches that partner technology-based solutions with biomedical and biobehavioral research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next-generation multidisciplinary science that encourages existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as networking, pervasive computing, advanced analytics, sensor integration, privacy and security, modeling of socio-behavioral and cognitive processes and system and process modeling. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, barriers to change, heterogeneity of data, semantic mismatch and limitations of current cyberphysical systems and an aging population. Such solutions demand multidisciplinary teams ready to address issues ranging from fundamental science and engineering to medical and public health practice.