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The Office of Advanced Cyberinfrastructure (OAC) supports translational research and education activities in all aspects of advanced cyberinfrastructure (CI) that lead to deployable, scalable, and sustainable systems capable of transforming science and engineering research. Advanced CI includes the spectrum of computational, data, software, networking, and security resources, tools, and services, along with the computational and data skills and expertise, that individually and collectively can transform science and engineering. OAC supports advanced CI research to address new CI frontiers for discovery leading to major innovations, and supports the development and deployment processes, as well as expert services, necessary for realizing the research CI that is critical to the advancement of all areas of science and engineering research and education.

The purpose of this amendment is to correct a typographical error in the abstract details on page 41. See the attached conformed BAA with changes highlighted in yellow. Amendment 01: The purpose of this amendment is to make administrative changes as highlighted in yellow in the attached.Original Synopsis Below:DARPA is soliciting innovative research proposals in the area of novel computing architectures. The Page 3 Architectures thrust of the Electronics Resurgence Initiative (ERI) seeks to demonstrate heterogeneous computing systems that provide the performance advantages of specialized processors, while maintaining the programmability of general purpose processors.The goal of the Software Defined Hardware (SDH) program is to build runtime-reconfigurable hardware and software that enables near ASIC performance without sacrificing programmability for data-intensive algorithms. SDH will create a hardware/software system that allows data-intensive algorithms to run at near ASIC efficiency without the cost, development time or single application limitations associated with ASIC development. The overall goal of the Domain-specific System on Chip (DSSoC) program is to develop a heterogeneous SoC comprised of many cores that mix general-purpose processors, special-purpose processors, hardware accelerators, memory, and input/output (I/O). DSSoC seeks to enable rapid development of multi-application systems through a single programmable device.

In celebrating its 350th anniversary, Merck KGaA, Darmstadt, Germany offers a series of research grants to stimulate innovative research in challenging areas of future importance. Merck KGaA, Darmstadt, Germany intends to provide several research grants of up to EUR 350,000 per year for 3 years in various research areas with the option of extension or expansion. Grants are offered for research in the following areas: Healthy Lives / Drug Discovery: Challenge 1: What is the next game-changing molecule or technology to help cure cancer or autoimmune disease? Life Reimagined / Synthetic Biology: Challenge 1: What is the next generation production technology for biologics? Challenge 2: Can you revolutionize microbiome research? Materials & Solutions: Challenge 1: Can you develop a new generation of intelligent materials? Challenge 2: Can you develop advances in characterization, control and surface chemistry? Challenge 3: Can you develop better atomic layer processes - from modelling to materials? Digitalization / Computing: Challenge 1: How can in-silico research benefit from deep learning or quantum Computing?

The IMEE program supports fundamental, multidisciplinary research on the impact of hazards and disasters upon civil infrastructure and society. The program is focused upon research on the mitigation of, preparedness for, response to, and recovery from multi-hazard disasters. Community and societal resilience and sustainability are important topics within the research portfolio of IMEE. The program is deeply multidisciplinary, integrating multiple perspectives, methods and results from diverse areas in engineering, social and natural sciences, and computing. Among these are civil, mechanical, transportation and system engineering; sociology, cognitive science and psychology, economics, geography, political science and urban planning; geology, biology and meteorology; and applied computing. Methodological innovations that span multiple, diverse disciplines are strongly encouraged.

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.

The National Science Foundation's (NSF’s) Directorates for Computer & Information Science & Engineering (CISE) and Mathematical & Physical Sciences (MPS) recently launched the Transdisciplinary Research in Principles of Data Science (TRIPODS) Phase Iprogram with the goal of promoting long-term, interdisciplinary research and training activities that engage theoretical computer scientists, statisticians, and mathematicians in developing the theoretical foundations of data science. Twelve TRIPODS Phase I Institutes were established in FY17 (see https://www.nsf.gov/news/news_summ.jsp?cntn_id=242888). The Partnerships between Science and Engineering Fields and the NSF TRIPODS Institutes (TRIPODS + X) solicitation seeks to expand the scope of the TRIPODS program beyond the foundations community by engaging researchers across other NSF disciplines and the TRIPODS research teams in collaborative activities. TRIPODS + X projects will foster relationships between researchers in science & engineering domains and foundational data scientists by leveraging existing NSF investments in the TRIPODS organizations. Working in concert with a TRIPODS organization, a TRIPODS + X project would focus on data-driven research challenges motivated by applications in one or more science and engineering domains or other activities aimed at building robust data science communities.

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The National Science Foundation (NSF) Directorates for Engineering (ENG) and Geosciences (GEO), the Divisions of Integrative Organismal Systems (IOS) and Environmental Biology (DEB), in the Directorate for Biological Sciences (BIO), the Division of Computer and Network Systems in the Directorate Computer and Information Science and Engineering (CISE/CNS), and the Division of Chemistry (CHE) in the Directorate for Mathematical and Physical Sciences, in collaboration with the US Department of Agriculture National Institute of Food and Agriculture (USDA NIFA) encourage convergent research that transforms existing capabilities in understanding dynamic soil processes, including soil formation, through advances in sensor systems and modeling. The Signals in the Soil (SitS) program fosters collaboration among the two partner agencies and the researchers they support by combining resources and funding for the most innovative and high-impact projects that address their respective missions. To make transformative advances in our understanding of soils, multiple disciplines must converge to produce environmentally-benign novel sensing systems with multiple modalities that can adapt to different environments and collect and transmit data for a wide range of biological, chemical, and physical parameters. Effective integration of sensor data will be key for achieving a better understanding of signaling interactions among plants, animals, microbes, the soil matrix, and aqueous and gaseous components. New sensor networks have the potential to inform models in novel ways, to radically change how data is obtained from various natural and managed (both urban and rural) ecosystems, and to better inform the communities that directly rely on soils for sustenance and livelihood.

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The National Science Foundation (NSF) Directorates for Engineering (ENG) and Geosciences (GEO), the Divisions of Integrative Organismal Systems (IOS) and Environmental Biology (DEB), in the Directorate for Biological Sciences (BIO), the Division of Computer and Network Systems in the Directorate Computer and Information Science and Engineering (CISE/CNS), and the Division of Chemistry (CHE) in the Directorate for Mathematical and Physical Sciences, in collaboration with the US Department of Agriculture National Institute of Food and Agriculture (USDA NIFA) encourage convergent research that transforms existing capabilities in understanding dynamic soil processes, including soil formation, through advances in sensor systems and modeling. The Signals in the Soil (SitS) program fosters collaboration among the two partner agencies and the researchers they support by combining resources and funding for the most innovative and high-impact projects that address their respective missions. To make transformative advances in our understanding of soils, multiple disciplines must converge to produce environmentally-benign novel sensing systems with multiple modalities that can adapt to different environments and collect and transmit data for a wide range of biological, chemical, and physical parameters. Effective integration of sensor data will be key for achieving a better understanding of signaling interactions among plants, animals, microbes, the soil matrix, and aqueous and gaseous components. New sensor networks have the potential to inform models in novel ways, to radically change how data is obtained from various natural and managed (both urban and rural) ecosystems, and to better inform the communities that directly rely on soils for sustenance and livelihood.

The purpose of this funding opportunity announcement (FOA) is twofold: 1. to accelerate translational and clinical Phase I and II a/b safety and efficacy studies for substantiating measurable functional benefits of probiotic/prebiotic components and/or their combinations; and; 2. to understand the underlying mechanisms of their action(s), and variability in responses to these interventions. This FOA calls for interdisciplinary collaborations across scientific disciplines engaged in microbiome and pro/prebiotic research including, but not limited to: nutritional science, microbiology, virology, microecology and microbiome, genomics, immunology, computational biology, chemistry, bioengineering, as well as integration of omics and computational approaches in DNA technologies.

The Engineering Design and Systems Engineering (EDSE) program supports fundamental research into the basic processes and phenomena of engineering design and systems engineering. The program seeks proposals leading to improved understanding about how processes, organizational structure, social interactions, strategic decision making, and other factors impact success in the planning and execution of engineering design and systems engineering projects. It also supports advances pertaining to engineering design and systems engineering in areas that include, but are not limited to, decision making under uncertainty, including preference and demand modeling; problem decomposition and decision delegation; applications of reverse game theory (mechanism design); computer-aided design; design representation; system performance modeling and prediction; design optimization; uncertainty quantification; domain- or concern-specific design methods; and advanced computational techniques for supporting effective human cognition, decision making, and collaboration. Competitive proposals for novel methods will include a plan to evaluate rigorously the effectiveness and performance of the proposed approach. The EDSE program encourages multidisciplinary collaborations of experts in design and systems engineering with experts in other domains. Of particular interest is research on the design of engineering material systems that leverages the unique aspects of a particular material system to realize advanced design methods that are driven by performance metrics and incorporate processing/manufacturing considerations.

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

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

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composingheterogeneous computing componentsand continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid stridesin machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred.

CISE's Division of Computing and Communication Foundations (CCF) supports research and education projects that develop new knowledge in three core programs: -The Algorithmic Foundations (AF) program; -The Communications and Information Foundations (CIF) program; -The Software and Hardware Foundations (SHF) program.

The Office of Science (SC) of the Department of Energy hereby announces its continuing interest in receiving grant applications for support of work in the following program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, and Nuclear Physics.

The Office of Science (SC) of the Department of Energy hereby announces its continuing interest in receiving grant applications for support of work in the following program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, and Nuclear Physics.

Transdisciplinary Research In Principles Of Data Science (TRIPODS) aims to bring together the statistics, mathematics, and theoretical computer science communities to develop the theoretical foundations of data science through integrated research and training activities. Phase I, described in this solicitation, will support the development of small collaborative Institutes. Phase II (to be described in an anticipated future solicitation, subject to availability of funds) will support a smaller number of larger Institutes, selected from the Phase I Institutes via a second competitive proposal process. All TRIPODS Institutes must involve significant and integral participation by all three of the aforementioned communities.

Transdisciplinary Research In Principles Of Data Science (TRIPODS) aims to bring together the statistics, mathematics, and theoretical computer science communities to develop the theoretical foundations of data science through integrated research and training activities. Phase I, described in this solicitation, will support the development of small collaborative Institutes. Phase II (to be described in an anticipated future solicitation, subject to availability of funds) will support a smaller number of larger Institutes, selected from the Phase I Institutes via a second competitive proposal process. All TRIPODS Institutes must involve significant and integral participation by all three of the aforementioned communities.