The Square Kilometre Array, in conjunction with Amazon Web Services (AWS), is pleased to issue this call for proposals for grants to use AWS for radio astronomy data reduction or tools and techniques development.  AWS has made a significant tranche of AWS Services credits available each year, for two years for this purpose, and will host up to 1 Petabyte of radio astronomy datasets as a public resource open to grant recipients and the community in general.

The HHPR Summit is an international meeting supporting human systems research held in Dayton, Ohio with the objective of linking defense and academic research to the commercial domains. The Summit is a forum to generate research knowledge and ideas, recruit and retain key organizations and researchers in the human systems domain, and link research to entrepreneurial outcomes, and diffuse information.

This program provides fellowships, through academic departments and programs of IHEs, to assist graduate students with excellent records who demonstrate financial need and plan to pursue the highest degree available in their course study at the institution in a field designated as an area of national need.

The Department of Energy National Energy Technology Laboratory, on behalf of the Office of Electricity Delivery and Energy Reliability, is seeking applications to enable an academic collaboration with expertise in power system engineering and cyber security computer science to innovate and transition cyber security capabilities to the energy sector to reduce the risk of power disruption resulting from a cyber-incident.

Researchers in all fields of science and engineering are being challenged in two key directions.  The first challenge is to push beyond the current boundaries of knowledge to provide ever-deeper insights through fundamental disciplinary research by addressing increasingly complex questions, which often requires extremely sophisticated integration of theoretical, experimental, observational and simulation and modeling results.   These efforts, which have relied heavily on observing platforms and other data collection efforts, computing facilities, software, advanced networking, analytics, visualization and models have led to important breakthroughs in all areas of science and engineering and represent a very strong bottom-up approach to the necessary research infrastructure.  The second, and more extensive challenge, is to synthesize these fundamental ground breaking efforts across multiple fields to transform scientific research into an endeavor that integrates the deep knowledge and research capabilities developed within the universities, industry and government labs. Individuals, teams and communities need to be able work together; likewise, instruments, facilities (including MREFCs), datasets, and cyber-services must be integrated from the group to campus to national scale. One can imagine secure, geographically distributed infrastructure components including advanced computing facilities, scientific instruments, software environments, advanced networks, data storage capabilities, and the critically important human capital and expertise. Greater understanding is also needed of how scientific and research communities will evolve in the presence of new cyberinfrastructure. 

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.

Advancements in data-driven scientific research depend on trustworthy and reliable cyberinfrastructure. Researchers rely on a variety of networked technologies and software tools to achieve their scientific goals. These may include local or remote instruments, wireless sensors, software programs, operating systems, database servers, high-performance computing, large-scale storage arrays, and other critical infrastructure connected by high-speed networking. This complex, distributed, interconnected global cyberinfrastructure ecosystem presents unique cybersecurity challenges. NSF-funded scientific instruments are specialized, highly visible assets that present attractive targets for both unintentional errors and malicious activity; untrustworthy software or a loss of integrity of the data collected by a scientific instrument may mean corrupt, skewed or incomplete results. Furthermore, often data-driven research, e.g., in the medical field or in the social sciences, requires access to private information, and exposure of such data may cause financial, reputational and/or other damage. Therefore, an increasing area of focus for NSF is the development and deployment of hardware and software technologies and techniques to protect research cyberinfrastructure across every stage of the scientific workflow.

The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, geosciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science. The solicitation invites two types of proposals: "Foundations" (F): those developing or studying fundamental theories, techniques, methodologies, technologies of broad applicability to Big Data problems; and "Innovative Applications" (IA): those developing techniques, methodologies and technologies of key importance to a Big Data problem directly impacting at least one specific application. Therefore, projects in this category must be collaborative, involving researchers from domain disciplines and one or more methodological disciplines, e.g., computer science, statistics, mathematics, simulation and modeling, etc. While Innovative Applications (IA) proposals may address critical big data challenges within a specific domain, a high level of innovation is expected in all proposals and proposals should, in general, strive to provide solutions with potential for a broader impact on data science and its applications. IA proposals may focus on novel theoretical analysis and/or on experimental evaluation of techniques and methodologies within a specific domain. Proposals in all areas of sciences and engineering covered by participating directorates at NSF are welcome.

The Mahoney Prize recognizes an outstanding article in the history of computing and information technology, broadly conceived. The Mahoney Prize commemorates the late Princeton scholar Michael S. Mahoney, whose profound contributions to the history of computing came from his many articles and book chapters. The prize consists of a $500 award and a certificate. For the inaugural 2015 prize, articles published in the preceding three years (2012, 2013, and 2014) are eligible for nomination. The Mahoney Prize is awarded by the Special Interest Group in Computers, Information, and Society (SIGCIS) and is presented during the annual meeting of our parent group, the Society for the History of Technology.

Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability that will far exceed the simple embedded systems of today. CPS technology will transform the way people interact with engineered systems -- just as the Internet has transformed the way people interact with information. New smart CPS will drive innovation and competition in sectors such as agriculture, energy, transportation, building design and automation, healthcare, and manufacturing. The December 2010 report of the President's Council of Advisors on Science and Technology (PCAST) titled Designing a Digital Future: Federally Funded Research and Development in Networking and Information Technologycalls for continued investment in CPS research because of its scientific and technological importance as well as its potential impact on grand challenges in a number of sectors critical to U.S. security and competitiveness such as the ones noted above. These challenges and technology gaps are further described in aCPS Vision Statementpublished in 2012 by the federal Networking and Information Technology Research and Development (NITRD) CPS Senior Steering Group. Tremendous progress has been made in advancing CPS technology over the last five-plus years. We have explored foundational technologies that have spanned an ever-growing set of application domains, enabling breakthrough achievements in many of these fields. At the same time, the demand for innovation in these domains continues to grow, and is driving the need to accelerate fundamental research to

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 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. 

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

CISE’s Division of Computer and Network Systems (CNS) supports research and education projects that develop new knowledge in two core programs:Computer Systems Research (CSR) program; andNetworking Technology and Systems (NeTS) program.Proposers are invited to submit proposals in three project classes, which are defined as follows:Small Projects - up to $500,000 total budget with durations up to three years;Medium Projects - $500,001 to $1,200,000 total budget with durations up to four years; andLarge Projects - $1,200,001 to $3,000,000 total budget with durations up to five years.A more complete description of the three project classes can be found in section II. Program Description of this document.

The advancement of sensing technology such as RGBD (Red Green Blue Depth), multi-camera and light field imaging systems, networks of sensors, advanced visual analytics and cloud computing will challenge the longstanding paradigms of capturing, creating, analyzing and utilizing visual information. Advances in Visual and Experiential Computing (VEC) will enable capability, adaptability, scalability, and usability that will far exceed the simple information systems of today. VEC technology will transform the way people interact with visual information through, for example, the realization of new mobile and wearable devices and the emergence of autonomous machines and semantically aware spaces. VEC research will drive innovation and competition in many industrial sectors as well as enhance the quality of life for ordinary people. Fast growing visual data has become a bottleneck in human decision processes in several emergent situations. New VEC technology is crucial to extracting information from complex visual and related data sets, combining this information with intuitive modes of human perception, and generating actionable information. The goal of this joint solicitation between NSF and Intel is to foster novel, transformative, multidisciplinary approaches that promote research in VEC technologies, taking into consideration the various challenges present in this field. This solicitation aims to foster a research community committed to advancing research and education at the confluence of VEC technologies, and to transitioning its findings into practice. NSF and Intel will support three types of projects, each three years in duration: Small projects with funding from $500,000 to $1,000,000 per project; Medium projects with funding from $1,000,001 to $2,000,000 per project; and Large projects with funding from $2,000,001 to $3,000,000.?? It is intended that NSF and Intel will cofund each project in equal amounts. This NSF/Intel partnership combines CISE??s experience

The scope is science and technology development, experimentation, and demonstration in the areas of improving and personalizing individual, team, and larger instructional training methods to include the following: - Competency definition and requirements analysis - Measuring, diagnosing, and modeling human expertise and performance - Rapid development of models of human cognition - Specifying and validating, both empirically and practically, new classes of synthetic, computer-generated agents and teammates - Training and rehearsal strategies and models - Environments that support learning and proficiency achievement and sustainment during non-practice or under novel contexts

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) applied 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, 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 the algorithmic contributions and resulting applications, tools, languages, compilers, libraries, architectures, systems, data, etc.

With this Dear Colleague letter (DCL), the NSF is announcing its intention to accept EArly-Concept Grants for Exploratory Research (EAGER) proposals to support NSF researchers in participating in the NIST GCTC teams, with the goal of pursuing novel research on effective integration of networked computer systems and physical devices that will have significant impact in meeting the challenges of the smart city. Priority will be given to researchers who have previously received funding from CPS, or who have related projects from other NSF programs (e.g., Computer Systems Research (CSR), Energy, Power, Control and Networks (EPCN), Secure and Trustworthy Cyberspace (SaTC), including CAREER awardees), and who are members of, or are seeking to, establish GCTC teams building upon the results of NSF-funded projects.

With this Dear Colleague letter (DCL), the NSF is announcing its intention to accept EArly-Concept Grants for Exploratory Research (EAGER) proposals to support NSF researchers in participating in the NIST GCTC teams, with the goal of pursuing novel research on effective integration of networked computer systems and physical devices that will have significant impact in meeting the challenges of the smart city. Priority will be given to researchers who have previously received funding from CPS, or who have related projects from other NSF programs (e.g., Computer Systems Research (CSR), Energy, Power, Control and Networks (EPCN), Secure and Trustworthy Cyberspace (SaTC), including CAREER awardees), and who are members of, or are seeking to, establish GCTC teams building upon the results of NSF-funded projects.

NSF is interested in supporting activities by the NSF Cyberinfrastructure community in the analysis of existing benchmarks, and in the development of new benchmarks, that measure real-world performance and effectiveness of large-scale computing systems for science and engineering discovery. Research, development, and use of performance benchmarks in high-performance computing (HPC) has been active for over 20 years, as evidenced by the development of LINPACK and the emergence of the TOP500 list in the early 1990s, followed by the development of the HPC Challenge Benchmark and the current HPCG effort (http://tiny.cc/hpcg). There have been efforts to provide benchmarks that include real applications, such as the SPEC High Performance Computing Benchmarks (http://spec.org/benchmarks.html#hpg), the Blue Waters SPP suite (http://www.ncsa.illinois.edu/assets/pdf/news/BW1year_apps.pdf), and the NERSC SSP (https://www.nersc.gov/users/computational-systems/nersc-8-system-cori/nersc-8-procurement/trinity-nersc-8-rfp/nersc-8-trinity-benchmarks/ssp/). Recent efforts have sought to broaden the set of relevant benchmarks to more effectively cover performance under different application environments such as data-intensive analysis (e.g., Graph500). Energy efficiency has also emerged in recent years as a relevant and increasingly important area of measurement and profiling for HPC systems (e.g., Green500). In addition to HPC, the Big Data community has gained interest in benchmarking; reference approaches to measuring and characterizing system performance for large-scale data analysis hardware and software systems remains an area of research, development, and community discussion (e.g., on the Big Data Top 100). Industry and academe have convened an ongoing series of workshops and meetings on the topic of Big Data benchmarking (http://clds.ucsd.edu/bdbc/workshops). Given the emergence of inference-based computing, the growing role of data analysis, changes in scientific workflow du

NSF is interested in supporting activities by the NSF Cyberinfrastructure community in the analysis of existing benchmarks, and in the development of new benchmarks, that measure real-world performance and effectiveness of large-scale computing systems for science and engineering discovery.