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PIF provides support for physics proposals in three subareas: 1) computational physics, 2) data enabled physics, and 3) quantum information science and revolutionary computing. Computational physics emphasizes methods for high-performance computing that require significant code development, are led by physicists, and may include applied mathematicians and computer scientists. Priority will be given to proposals that, in addition to compelling scientific goals, have a computational advance or new enabling capability. Proposals should include either innovation in computing such as algorithm development and efficient use of novel architectures or provide significant improvement to community codes. Data enabled physics seeks proposals to develop tools and infrastructure that provide rapid, secure, and efficient access to physics data stores via heterogeneous or distributed computing resources and networks. Examples include development of reliable digital preservation, access, integration, and curation capabilities associated with data from Physics Division experimental facilities and the tools for data handling needed to maximize the scientific payoff. Priority will be given to proposals that serve broad communities or that bring dramatic new capabilities to a specific sub-area of physics. Quantum information and revolutionary computing supports theoretical and experimental proposals that explore applications of quantum mechanics to new computing paradigms or that foster interactions between physicists, mathematicians, and computer scientists that push the frontiers of quantum-based information, transmission, and manipulation.

Algorithms in the Field encourages closer collaboration between two groups of researchers: (i) theoretical computer science researchers, who focus on the design and analysis of provably efficient and provably accurate algorithms for various computational models; and (ii) other computing and information researchers including a combination of systems and domain experts (very broadly construed - including but not limited to researchers in computer architecture, programming languages and systems, computer networks, cyber-physical systems, cyber-human systems, machine learning, artificial intelligence and its applications, database and data analytics, etc.) who focus on the particular design constraints of applications and/or computing devices. Each proposal must have at least one co-PI interested in theoretical computer science and one interested in any of the other areas typically supported by CISE. Proposals are expected to address the dissemination of both the algorithmic contributions and the resulting applications, tools, languages, compilers, libraries, architectures, systems, data, etc.

Algorithms in the Field encourages closer collaboration between two groups of researchers: (i) theoretical computer science researchers, who focus on the design and analysis of provably efficient and provably accurate algorithms for various computational models; and (ii) other computing and information researchers including a combination of systems and domain experts (very broadly construed - including but not limited to researchers in computer architecture, programming languages and systems, computer networks, cyber-physical systems, cyber-human systems, machine learning, artificial intelligence and its applications, database and data analytics, etc.) who focus on the particular design constraints of applications and/or computing devices. Each proposal must have at least one co-PI interested in theoretical computer science and one interested in any of the other areas typically supported by CISE. Proposals are expected to address the dissemination of both the algorithmic contributions and the resulting applications, tools, languages, compilers, libraries, architectures, systems, data, etc.

Algorithms in the Field encourages closer collaboration between two groups of researchers: (i) theoretical computer science researchers, who focus on the design and analysis of provably efficient and provably accurate algorithms for various computational models; and (ii) other computing and information researchers including a combination of systems and domain experts (very broadly construed - including but not limited to researchers in computer architecture, programming languages and systems, computer networks, cyber-physical systems, cyber-human systems, machine learning, artificial intelligence and its applications, database and data analytics, etc.) who focus on the particular design constraints of applications and/or computing devices. Each proposal must have at least one co-PI interested in theoretical computer science and one interested in any of the other areas typically supported by CISE. Proposals are expected to address the dissemination of both the algorithmic contributions and the resulting applications, tools, languages, compilers, libraries, architectures, systems, data, etc.

Algorithms in the Field encourages closer collaboration between two groups of researchers: (i) theoretical computer science researchers, who focus on the design and analysis of provably efficient and provably accurate algorithms for various computational models; and (ii) other computing and information researchers including a combination of systems and domain experts (very broadly construed - including but not limited to researchers in computer architecture, programming languages and systems, computer networks, cyber-physical systems, cyber-human systems, machine learning, artificial intelligence and its applications, database and data analytics, etc.) who focus on the particular design constraints of applications and/or computing devices. Each proposal must have at least one co-PI interested in theoretical computer science and one interested in any of the other areas typically supported by CISE. Proposals are expected to address the dissemination of both the algorithmic contributions and the resulting applications, tools, languages, compilers, libraries, architectures, systems, data, etc.

Increasingly, data- and compute-intensive research and education efforts are benefiting from access to cloud computing platforms, which provide robust, agile, reliable, and scalable infrastructure. To better support this growing use of cloud computing resources, the National Science Foundation's (NSF) Directorate for Computer and Information Science and Engineering (CISE) seeks to fund an entity that can serve as a principal interface between the CISE research and education community and public cloud computing providers. Through this solicitation, NSF will support an entity that will have multiple responsibilities, including: 1) establishing partnerships with the various public cloud computing providers; 2) assisting NSF in allocating cloud computing resources to qualifying CISE-funded projects; 3) managing cloud computing accounts and resources allocated to individual CISE projects; 4) providing user training and other support to CISE researchers and educators using cloud computing in their work; and 5) providing strategic technical guidance for CISE researchers and educators interested in using public cloud computing platforms.

Increasingly, data- and compute-intensive research and education efforts are benefiting from access to cloud computing platforms, which provide robust, agile, reliable, and scalable infrastructure. To better support this growing use of cloud computing resources, the National Science Foundation's (NSF) Directorate for Computer and Information Science and Engineering (CISE) seeks to fund an entity that can serve as a principal interface between the CISE research and education community and public cloud computing providers. Through this solicitation, NSF will support an entity that will have multiple responsibilities, including: 1) establishing partnerships with the various public cloud computing providers; 2) assisting NSF in allocating cloud computing resources to qualifying CISE-funded projects; 3) managing cloud computing accounts and resources allocated to individual CISE projects; 4) providing user training and other support to CISE researchers and educators using cloud computing in their work; and 5) providing strategic technical guidance for CISE researchers and educators interested in using public cloud computing platforms.

The Algorithmic Foundations (AF) program supports potentially transformative research and education projects advancing design and analysis of algorithms and characterized by algorithmic thinking accompanied by rigorous analysis. Research on algorithms for problems that are central to computer science and engineering, as well as new techniques for the rigorous analysis of algorithms, are of interest. AF supports theoretical research that bounds the intrinsic difficulty of problems to determine the measures of complexity in formal models of computation, classical or new. The goal is to understand the fundamental limits of resource-bounded computation and to obtain efficient algorithms operating within those limits. The time and space complexity of finding exact and approximate solutions in deterministic and randomized models of computation is a central concern of the program; research on resources other than time and space, such as communication and energy, is also encouraged. In addition to the traditional, sequential computing paradigm, AF supports research on the design and analysis of novel algorithms in parallel and distributed models, in particular, in heterogeneous multi-core and many-core machines; the computational models and algorithms that capture essential aspects of computing over massive data sets; and alternative forms of computation and information processing, including quantum computing and biological models of computation.

Physics at the Information Frontier (PIF) includes support for data-enabled science, community research networks, and new computational infrastructure, as well as for next-generation computing. It focuses on cyber-infrastructure for the disciplines supported by the Physics Division while encouraging broader impacts on other disciplines. Disciplines within the purview of the Physics Division include: atomic, molecular, optical, plasma, elementary particle, nuclear, particle astrophysics, gravitational and biological physics. Proposals with intellectual focus in areas supported by other NSF Divisions should be submitted to those divisions directly. Proposals that cross Divisional lines are welcome, but the Physics Division encourages PIs to request a co-review by naming other divisional programs on the cover sheet. This facilitates the co-review and participation of other programs in the review process. PIF provides support for physics proposals in three subareas: 1) computational physics, 2) data enabled physics, and 3) quantum information science and revolutionary computing. Computational physics emphasizes methods for high-performance computing that require significant code development, are led by physicists, and may include applied mathematicians and computer scientists. Priority will be given to proposals that, in addition to compelling scientific goals, have a computational advance or new enabling capability. Proposals should include either innovation in computing such as algorithm development and efficient use of novel architectures or provide significant improvement to community codes.Data enabled physics seeks proposals to develop tools and infrastructure that provide rapid, secure, and efficient access to physics data stores via heterogeneous or distributed computing resources and networks.

SoftwareInfrastructure for Sustained Innovation (SI2) is a long-term investment focused on realizing a portion of the Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21, http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504730) vision and catalyzing new thinking, paradigms and practices in science and engineering. CIF21 envisions a linked cyberinfrastructure architecture that integrates large-scale computing, high-speed networks, massive data archives, instruments and major facilities, observatories, experiments, and embedded sensors and actuators, across the nation and the world, and that enables research at unprecedented scales, complexity, resolution, and accuracy by integrating computation, data, and experiments in novel ways. Software is a primary modality through which CIF21 innovation and discovery will be realized. It permeates all aspects and layers of cyberinfrastructure (from application codes and frameworks, programming systems, libraries and system software, to middleware, operating systems, networking and the low-level drivers). The CIF21 software infrastructure must address the complexity of this cyberinfrastructure, accommodating: disruptive hardware trends; ever-increasing data volumes; data integrity, privacy, and confidentiality; security; complex application structures and behaviors; and emerging concerns such as fault-tolerance and energy efficiency. The programs must focus on building robust, reliable and sustainable software that will support and advance sustained scientific innovation and discovery.
 The Division of Advanced Cyberinfrastructure in the Computer & Information Science & Engineering Directorate (CISE/ACI) is partnering with Directorates and Offices across the NSF to support SI2, a long-term comprehensive program focused on realizing a sustained software infrastructure that is an integral part of CIF21.

Innovation in information technology (IT) has driven economic growth, underlies many of our recent scientific advances, and ensures our national security; it is not surprising then that predicted IT job growth is very strong. Yet students are not majoring in computing in sufficient numbers to fulfill the forecasted demand. This shortfall is exacerbated by the longstanding underrepresentation of women, persons with disabilities, African Americans, Hispanics, Native Americans and indigenous peoples in computing. We cannot meet workforce demands without their participation and we cannot, in an increasingly competitive world economy, afford to cede the talents and creativity of so many. To ensure their participation, and the full participation of all students, we must provide better opportunities to study computing in K-12. We must start with a better understanding of how students learn computing. Unlike many of the other STEM (science, technology, engineering, and mathematics) disciplines, computing has not developed a robust research base on the teaching and learning of its fundamental concepts and skills. That research base must be built and it must be used in providing all students with rigorous academic curricula that cover computational concepts and skills, and the breadth of application and potential of computing. Providing access to rigorous, academic computing in K-12 will require an unprecedented effort to develop curriculum and materials and to prepare teachers.

As computing has become an integral part of the practice of modern science, technology, engineering and mathematics (STEM), the STEM + Computing Partnerships program seeks to address the urgent need to prepare students from the early grades through high school in the essential skills, competencies, and dispositions needed to succeed in a computationally-dependent world. Thus, STEM+C advances the integration of computational thinking and computing activities in early childhood education through high school (pre-K-12) to provide a strong and developmental foundation in computing and computational thinking through the integration of computing in STEM teaching and learning, and/or the applied integration of STEM content in pre-K-12 computer science education.

The Directorate for Engineering (ENG) and the Directorate for Computer and Information Science and Engineering (CISE),  Research Experiences for Teachers (RET) in Engineering and Computer Science program supports the active involvement of K-12 science, technology, engineering, computer and information science, and mathematics (STEM) teachers and community college faculty in engineering and computer science research in order to bring knowledge of engineering, computer science, and technological innovation into their classrooms. The goal is to help build long-term collaborative partnerships between K-12 STEM teachers, community college faculty, and the NSF university research community by involving the teachers and community college faculty in engineering and computer science research and helping them translate their research experiences and new knowledge into classroom activities.  Partnerships with inner city schools or other high needs schools are especially encouraged, as is participation by underrepresented minorities, women, and persons with disabilities. This announcement features two mechanisms for support of in-service and pre-service K-12 STEM teachers and community college faculty: RET supplements to ongoing ENG or CISE awards and new RET Site awards. RET supplements may be included in proposals for new or renewed NSF Directorate for Engineering (ENG) or CISE grants or as supplements to ongoing NSF ENG or CISE funded projects. RET in Engineering and Computer Science Sites are based on independent proposals from engineering or computer and information science departments, schools or colleges to initiate and conduct research participation projects for a number of K-12 STEM teachers and/or community college faculty.

Synopsis of Program: The Directorate for Engineering (ENG) and the Directorate for Computer and Information Science and Engineering (CISE), have joined to support the Research Experiences for Teachers (RET) in Engineering and Computer Science program. This program supports active long-term collaborative partnerships between K-12 Science, Technology, Engineering, Computer and Information Science, and Mathematics (STEM) teachers and community college and university faculty and students to bring knowledge of engineering or computer and information science and engineering as well as technological innovation to pre-college/community college classrooms. The goal of these partnerships is to enable K-12 STEM teachers and community college faculty to translate their research experiences and new knowledge gained in university settings into their classroom activities. The university team will include faculty, graduate and undergraduate students as well as industrial advisors. Involvement of graduate students in support of academic-year classroom activities is particularly encouraged. Partnerships with inner city, rural or other high needs schools are especially encouraged, as is participation by underrepresented minorities, women, and persons with disabilities. As part of the long-term partnership arrangements, university undergraduate/graduate students will partner with pre-college/community college faculty in their classrooms during the academic year to help teach engineering/computer science concepts. This announcement features two mechanisms for support of in-service and pre-service K-12 STEM teachers and community college faculty: (1) RET supplements to ongoing ENG and CISE awards and (2) new RET Site awards. RET supplements may be included outside this solicitation in proposals for new or renewed NSF Directorate for Engineering (ENG) and Directorate for Computer and Information Science and Engineering (CISE) grants or as supplements to ongoing NSF ENG and CISE funded pro

Computer systems support a broad range of applications and technologies that seamlessly integrate with human users. While many key building blocks of computer systems are today commercial technologies, the challenge ahead is to envision new technologies, as well as to combine existing technologies, software, and sensing systems into the computer systems of the future that will span wearable computing, the Internet of Things (IoT), "Smart Cities," intelligent transportation systems, personalized healthcare, and beyond. Such computer systems will require new, innovative, and visionary approaches to hardware, wired and wireless communications, consideration of human-computer interactions, and new programming languages and compilers that are limited only by the imagination. They will need to be reliable in the presence of unreliable components, adaptive to changing environments, capable of supporting high-throughput applications and large-scale data storage and processing, and able to meet performance and energy objectives for applications ranging from very low-power embedded systems to large high-performance computing systems. Furthermore, computer systems of the future will need to provide mechanisms for ensuring security and privacy.

This Biological and Environmental Research/Advanced Scientific Computing Research (BERASCR) Scientific Discovery Thru Advanced Computing (SciDAC) Partnership FOA will enable scientists to conduct complex scientific and engineering computations at a level of fidelity needed to simulate real-world climate conditions, by supporting deep, necessary, and productive collaborations between climate scientists on the one hand and applied mathematicians and computer scientists on the other, that overcome the barriers between these disciplines and consequently fully exploit the capabilities of Department of Energy (DOE) High Performance Computing (HPC) systems in order to accelerate advances in climate science. This SciDAC opportunity targets three particular topics of high-priority for DOE climate research that are expected to be transformed by effective climate-computational partnerships: the development of new and innovative methods to predict sea-level change; the development of a theoretical statistical-numerical framework to improve climate prediction; and the development of improved methods for model component coupling. The next-generation climate model capabilities will contribute to the newly launched Accelerated Climate Model for Energy (ACME) and further its progress toward design of climate codes for leadership class computers and in support of energy science and mission requirements.

The Office of Basic Energy Sciences (BES) of the U.S. Department of Energy (DOE) announces its interest in receiving applications in Computational Materials Sciences proposing integrated, multidisciplinary teams that will perform research to develop validated community codes and data bases for predictive design of functional materials, excluding structural materials. Computational Materials Sciences Teams could also involve new approaches to enhance the use of large data sets derived from advanced characterization of materials, materials synthesis, processing, and properties assessments and the parallel data that are generated by large scale computational efforts that model materials phenomena. Computational Materials Sciences will support the Materials Genome Initiative for Global Competitiveness (MGI) that was announced by the President in June 2011. The goal of the MGI is to reduce the time from discovery to deployment of new materials by a factor of two and is tied to advancement of American manufacturing capability. This funding opportunity continues the BES commitment to the MGI and the development of open source codes that can take full advantage of today's petascale and future exascale leadership computing facilities.

The Office of Basic Energy Sciences (BES) of the U.S. Department of Energy (DOE) announces its interest in receiving applications in Computational Materials Sciences proposing integrated, multidisciplinary teams that will perform research to develop validated community codes and data bases for predictive design of functional materials, excluding structural materials. Computational Materials Sciences Teams could also involve new approaches to enhance the use of large data sets derived from advanced characterization of materials, materials synthesis, processing, and properties assessments and the parallel data that are generated by large scale computational efforts that model materials phenomena. Computational Materials Sciences will support the Materials Genome Initiative for Global Competitiveness (MGI) that was announced by the President in June 2011. The goal of the MGI is to reduce the time from discovery to deployment of new materials by a factor of two and is tied to advancement of American manufacturing capability. This funding opportunity continues the BES commitment to the MGI and the development of open source codes that can take full advantage of today's petascale and future exascale leadership computing facilities.

The Chemical Theory, Models and Computational Methods program supports the discovery and development of theoretical and computational methods or models to address a range of chemical challenges, with emphasis on emerging areas of chemical research.  Proposals that focus on established theoretical or computational approaches should involve innovative additions or modifications that substantially broaden their applicability.  Areas of interest include, but are not limited to, electronic structure, quantum reaction dynamics, statistical mechanics, molecular dynamics, and simulation and modeling techniques for molecular systems and systems in condensed phases.  Areas of application span the full range of chemical systems from small molecules to mesoscopic aggregates, including single molecules, biological systems and materials in condensed phases.   Despite the diverse application areas, the goal of the program is to support the development of new theoretical and computational methodologies that have the potential of being broadly applicable to a range of challenging chemical problems. We are particularly interested in fundamental areas of chemical research that are difficult or impossible to address using current synthetic, experimental, and/or computational methodologies.  We encourage the integration of innovative software development with methodological and algorithmic development, especially computational approaches that allow efficient utilization of the high end computers of the future.

The NSF's vision for Advanced Computing Infrastructure (ACI), which is part of its Cyberinfrastructure for 21st Century Science and Engineering (CIF21), focuses specifically on ensuring that the science and engineering community has ready access to the advanced computational and data-driven capabilities required to tackle the most complex problems and issues facing today's scientific and educational communities. To accomplish these goals requires advanced computational capabilities within the context of a multilevel comprehensive and innovative infrastructure that benefits all fields of science and engineering. Previous solicitations have concentrated on enabling petascale capability through the deployment and support of a world-class High Performance Computing (HPC) environment. In the past decade the NSF has provided the open science and engineering community with a number of state-of-the art HPC assets ranging from loosely coupled clusters, to large scale instruments with many thousands of computing cores communicating via fast interconnects, and more recently with diverse heterogeneous architectures. Recent developments in computational science have begun to focus on complex, dynamic and diverse workflows. Some of these involve applications that are extremely data intensive and may not be dominated by floating point operation speed. While a number of the earlier acquisitions have addressed a subset of these issues, the current solicitation emphasizes this even further.

This Funding Opportunity Announcement (FOA) solicits applications from single institutions, or consortia of institutions, to participate in a network of research groups developing computational models of immunity to infectious diseases other than HIV/AIDS. Applications are sought to develop, refine and validate computational models of immune responses (1) during or following infection, and/or (2) before and after vaccination against an infectious disease, through an iterative approach involving computational studies and immunological experimentation. The main goal of this FOA is to advance our understanding of the complex immune mechanisms triggered by infection and/or vaccination through the development and application of computational models of immunity, coupled with immunological experimentation to validate and improve the utility and robustness of the computational models.Another goal of this FOA is to make the computational models and data developed under this initiative readily available to the broader research community for further refinement or direct use in biological experimentation. This program will also support pilot projects, workshops, and symposia to foster the use of computational models of immunity by the broader research community.

This Funding Opportunity Announcement (FOA) solicits applications from single institutions, or consortia of institutions, to participate in a network of research groups developing computational models of immunity to infectious diseases other than HIV/AIDS. Applications are sought to develop, refine and validate computational models of immune responses (1) during or following infection, and/or (2) before and after vaccination against an infectious disease, through an iterative approach involving computational studies and immunological experimentation. The main goal of this FOA is to advance our understanding of the complex immune mechanisms triggered by infection and/or vaccination through the development and application of computational models of immunity, coupled with immunological experimentation to validate and improve the utility and robustness of the computational models.  Another goal of this FOA is to make the computational models and data developed under this initiative readily available to the broader research community for further refinement or direct use in biological experimentation. This program will also support pilot projects, workshops, and symposia to foster the use of computational models of immunity by the broader research community.  

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.