OARS funding Computer

Through this solicitation, OVC seeks to provide funding to organizations that demonstrate innovative strategies to create, expand, or enhance the use of technology on a national scale (or regional projects that can be replicated nationally) to improve responses, services, and access for victims of crime. OVC anticipates making up to three awards of up to $1.5 million each for a 24-month period of performance, to begin on October 1, 2020. OVC will conduct a pre-application webinar on Tuesday, March 31, 2020, from 1:00 to 2:00 p.m. e.t. Register at www.ovc.gov/grants/webinars.html. Apply by May 12, 2020.

This FOA invites applications to create an Artificial Intelligence and Technology Collaboratories for Aging Research Coordinating Center (AITCC), serving the needs of NIA and the NIAs Artificial Intelligence and Technology Collaboratories for Aging (AITC). The AITCC will serve as a hub for the AITC program and will facilitate and coordinate trans-AITC activities.

The Algorithms for Threat Detection (ATD) program supports research on new ways to use spatiotemporal datasets to develop quantitative models of human dynamics. The objectives include improved representation of complicated group dynamics and the development of algorithms that can process data in near real-time to accurately identify unusual events and forecast future threats indicated by those events.

The National Science Foundation (NSF), through its Divisions of Electrical, Communications and Cyber Systems (ECCS), Computing and Communication Foundations (CCF), Molecular and Cellular Biosciences (MCB), and Materials Research (DMR) announces a follow-up solicitation on the Semiconductor Synthetic Biology for Information Storage and Retrieval Program (SemiSynBio-II). Future ultra-low energy storage-based computing systems can be built on principles derived from organic systems that are at the intersection of physics, chemistry, biology, computer science and engineering. Next-generation information storage technologies can be envisioned that are driven by biological principles and use biomaterials in the fabrication of devices and systems that can store data for more than 100 years with storage capacity 1,000 times more than current storage technologies. Such a research effort can have a significant impact on the future of information storage and retrieval technologies. This focused solicitation seeks high-risk/high-return interdisciplinary research on novel concepts and enabling technologies that will address the fundamental scientific issues and technological challenges associated with the underpinnings of synthetic biology integrated with semiconductor technology. This research will foster interactions among various disciplines including biology, physics, chemistry, materials science, computer science and engineering that will enable in heretofore unanticipated breakthroughs.

This program aims to provide all U.S. students with the opportunity to participate in computer science (CS) and computational thinking (CT) education in their schools at the preK-12 levels. With this solicitation, the National Science Foundation (NSF) focuses on both research and researcher-practitioner partnerships (RPPs) that foster the research and development needed to bring CS and CT to all schools. Specifically, this solicitation aims to provide (1) high school teachers with the preparation, professional development (PD) and ongoing support they need to teach rigorous computer science courses; (2) preK-8 teachers with the instructional materials and preparation they need to integrate CS and CT into their teaching; and (3) schools and districts with the resources needed to define and evaluate multi-grade pathways in CS and CT.

In light of the emergence and spread of the coronavirus disease 2019 (COVID-19) in the United States and abroad, the National Science Foundation (NSF) is accepting proposals to conduct non-medical, non-clinical-care research that can be used immediately to explore how to model and understand the spread of COVID-19, to inform and educate about the science of virus transmission and prevention, and to encourage the development of processes and actions to address this global challenge. NSF encourages the research community to respond to this challenge through existing funding opportunities. In addition, we invite researchers to use the Rapid Response Research (RAPID) funding mechanism, which allows NSF to receive and review proposals having a severe urgency with regard to availability of or access to data, facilities or specialized equipment as well as quick-response research on natural or anthropogenic disasters and similar unanticipated events. Requests for RAPID proposals may be for up to $200K and up to one year in duration. Well-justified proposals that exceed these limits may be entertained.

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 composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in 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. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability,  domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation. 

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 Microsoft Foundation is inviting applications for its Dissertation Grants program. The program supports PhD students at North American universities who are underrepresented in the field of computing and pursuing research aligned to the research areas carried out by Microsoft Research. Through the program, recipients will receive funding of up to $25,000 for the 2020-21 academic year as well as an invitation to the PhD Summit, a two-day workshop in the fall held at one of Microsoft Research's labs where fellows will meet with Microsoft researchers and other top students to share their research. Fellows must be aligned in research areas as defined by Microsoft Research, which include artificial intelligence; audio and acoustics; computer vision; graphics and multimedia; human-computer interaction; human language technologies; search and information retrieval; data platforms and analytics; hardware and devices; programming languages and software engineering; security, privacy, and cryptography; systems and networking; algorithms; mathematics; ecology and environment; economics; medical, health, and genomics; social sciences; and technology for emerging markets.

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.

Address weaknesses in partner nations' export control, investment screening, critical infrastructure protection, customs and border security institutions to prevent malign state actors from exploiting those weaknesses for their advantage.

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 composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in 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. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability, domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation.

To request access to resources of the COVID-19 HPC Consortium, you must prepare a description, no longer than two pages, of your proposed work. To ensure your request is directed to the appropriate resource(s), your description should include the following sections. Do not include any proprietary information in proposals, since your request will be reviewed by staff from a number of consortium sites. It is expected that teams who receive Consortium access will publish their results in the open scientific literature.

The Core Infrastructure Fund (CIF) strives to uphold and increase capacity for individuals, organizations, and companies working to fortify the foundational components of demonstrably important technology relied upon by people in repressive countries. Ideal applications are: open in nature, collaborative, promote a broader understanding of existing challenges and limitations, are preemptive in approach, and/or exist at the core of the Internet's ecosystem. Common applicants come from the community of developers and organizers working on open-source projects recognized as critical dependencies of one or more active platforms or tools strengthening Internet freedom and digital security. Ideal applications for this fund focus on supporting: Key developers or organizers so they can work full time on crucial core efforts in need of additional support; New developers or organizers focused on improving security standards, quality assurance, and best practices within core infrastructure projects; Developers, authors, or organizers drafting or promoting digital security and civil society needs within standards and protocols; Researchers exploring new methods of circumvention that would improve the resiliency of widely utilized tools; Specific outcomes, such as the necessary maintenance and upgrades to existing open source projects (database, hosting, or other tool migration; rewriting test suites; major new features); Efforts that make existing projects more accessible and easier to contribute to (ex. documentation, tool migration, refactoring code, testing); Efforts that develop new or evolve existing organizational and governance structures and sustainability models beyond work-for-hire; Efforts that increase the understanding and awareness of relevant actors in this space, their roles, and how they contribute to maintaining the Internet's core ecosystem;

As part of our commitment to building the field of data science for social impact, data.org has launched a $10 million data.org Inclusive Growth and Recovery Challenge. Any strong and growing economy requires an inclusive and resilient approach to growth to ensure that talent, innovation, and our communities can become stronger in times of stress. By tapping into the expertise of a broad pool of thinkers and doers, we aim to catalyze innovative and scalable solutions to help individuals and communities thrive, all the while building resilience to withstand future challenges.

The worldwide growth of wireless communication, navigation, and telemetry has provided immense societal benefits including mobile broadband data, Internet of Things (IoT), mobile healthcare, and intelligent transportation systems. These and other applications including 5G and beyond wireless systems call for innovations that can circumvent the challenges of radio spectrum scarcity and interference and foster the growth of ubiquitous, high speed, low latency connectivity. Commercial applications like the above must operate in harmony with scientific uses such as research on radio astronomy, Earth and atmospheric sciences, and must not inhibit weather prediction, polar research, and other nationally vital activities, all of which are dependent upon access to the radio spectrum. The National Science Foundation (NSF) continues to support wireless spectrum research and the scientific uses of the electromagnetic spectrum through multiple programs that enable fast, accurate, dynamic coordination and usage of our limited spectrum resource. These programs have created an opportune ground to build and create a large center-based ecosystem for spectrum research, which is the target of this SII-Center program.

This Research Fund is opening a call for projects in the following areas: * Protecting vulnerable populations from epidemics and catastrophes, including COVID-19: be it migrants, informal settlements, workers in the informal economy, isolated people, people with disabilities, the elderly * Improving data collection and quality in health: how can data and technology help us get out of the crisis, understand it and mitigate it? How can it inform future containment and epidemic control? * Understanding the effects of confinement and social distancing: what are the effects of confinement and social distancing on society and households? What are the mental health consequences? The social and domestic repercussions? * Early warning and preparedness: how do we re-enforce our health infrastructure and ecosystem (including medical devices and drug supply) to be better prepared and how do we protect our health workers and caregivers? * Preserving the environment and our health: connections between climate change, biodiversity loss and the origin of viral disease including socioeconomic dynamics leading to infectious disease outbreaks and sanitary crisis; Learnings from COVID-19 for mitigating future related crises in climate and biodiversity

NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness, intended to be shared across all segments of society. Broad acceptance and adoption of large-scale deployments of AI systems rely critically on their trustworthiness which, in turn, depends on the ability to assess and demonstrate the fairness (including broad accessibility and utility), transparency, explainability, and accountability of such systems. For example, the behavior of algorithms for face recognition, speech, and language, especially when integrated into decision support systems applied across different segments of society, would benefit from new foundational research in fairness of AI systems. NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, algorithmic advances, fairness objectives, validation of fairness, and advances in broad accessibility and utility. Funded projects will enable broadened acceptance of AI systems, helping the U.S. further capitalize on the potential of AI technologies. Although Amazon provides partial funding for this program, it will not play a role in the selection of proposals for award.

Scientific machine learning is a core component of artificial intelligence and a computational technology that can be trained, with scientific data, to augment or automate human skills. Major research advances will be enabled by harnessing DOE investments in massive amounts of scientific data, software for predictive models and algorithms, high-performance computing (HPC) and networking platforms, and the national workforce. The crosscutting nature of machine learning and AI provides strong motivation for formulating a prioritized research agenda. Scientific Machine Learning and Artificial Intelligence (Scientific AI/ML) will have broad use and transformative effects across the research communities supported by DOE. Accordingly, a 2019 Basic Research Needs workshop report identified six Priority Research Directions. The first three PRDs describe foundational research themes that are common to the development of Scientific AI/ML methods and correspond to the need for domain-awareness, interpretability, and robustness. The other three PRDs describe capability research themes and correspond to the three major use cases of massive scientific data analysis (PRD #4), machine learning-enhanced

The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in research applications to explore potentially high-impact approaches in the development and use of artificial intelligence and machine learning in the context of computational decision support for complex systems.