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On the occasion of its 350th anniversary, Merck KGaA, Darmstadt, Germany will fund innovative projects in applied biophysical & analytical research. Projects will be based on the following challenges: · Challenge 1: Analytical technologies for antibodies and antibody-drug conjugates, as well as technologies to determine drug target engagement, or the level of protein or nucleic acid or metabolic biomarkers. · Challenge 2: Analytical technologies for display materials or semiconductors (Liquid Crystals, OLED Materials, Quantum Materials, Reactive Mesogens, Photoresist Materials). E.g. Spectroscopic Methods, MS Hyphenation, Capillary Chromatography and Comprehensive Separation Technologies; Surface analytics for displays (non- or destructive), Sample preparation techniques for ultra-sensitive investigations · Challenge 3: Analytical technologies for the characterization of polymers, chemical imaging, bioanalytical methods or new methods in molecular biology. Technologies of interest for example are: field flow floractionation (FFF), dynamic light scattering (DLS), chemical imaging, new chromatographic techniques, spectroscopic and spectrometric methods, electron paramagnetic resonance (EPR), microwave analytics

The Defense Advanced Research Projects Agency (DARPA) is soliciting innovative proposals in the area of global scale, multimodal geospatial data cloud platform and analytics development. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice. GCA is a 24-month, three-phase program. At present, DARPA seeks innovative proposals covering the tasks in Phase 1 (6 month base effort) and Phase 2 (12 month costed option) of the program. Proposals must address both Phase 1 and Phase 2. Phase 3 will be the subject of a separate procurement. For Phases 1 and 2, DARPA seeks proposals in two technical areas (TAs). TA-1 (Scalable Geospatial Data Platform) will provide access to geospatial data and an extensible computing platform on which TA-2 performers can efficiently access and process massive amounts of curated geospatial data. TA-2 (Analytical Applications and Competitions) will create software for use in one or more of the analytics competitions (predicting food shortages, locating fracking construction detection, illegal fishing detection, open call) using data and platforms provided by TA-1 proposers. In Phase 1, DARPA anticipates making up to three awards for TA-1 and up to 16 awards for TA-2. A proposer may respond to one or both technical areas but a separate proposal is required for each TA. A TA-2 proposer can propose to one or more competition areas, using costed options if proposing to more than one competition.

The CREATE program seeks proposals to develop, and experimentally test, systems that use crowdsourcing and structured analytic techniques (STs) to improve analytic reasoning. These systems will help people better understand the evidence and assumptions that support-or conflict with-conclusions. Secondarily, they will also help users better communicate their reasoning and conclusions. STs hold promise for increasing the logical rigor and transparency of analysis. They can help reveal underlying logic and identify unstated assumptions. Yet they are not widely used in the Intelligence Community or elsewhere-possibly because current versions are cumbersome or require too much time. Crowdsourcing has the potential to solve these problems by dividing the labor, allowing dispersed groups of analysts to contribute information and ideas where they have comparative advantages. Crowdsourcing can help analysts identify and understand alternative hypotheses, arguments, and points of view. Crowdsourcing of structured techniques may facilitate rational deliberation by integrating different perspectives, so that analysis can effectively benefit from "crowd wisdom."

The CREATE program seeks proposals to develop, and experimentally test, systems that use crowdsourcing and structured analytic techniques (STs) to improve analytic reasoning. These systems will help people better understand the evidence and assumptions that support-or conflict with-conclusions. Secondarily, they will also help users better communicate their reasoning and conclusions. STs hold promise for increasing the logical rigor and transparency of analysis. They can help reveal underlying logic and identify unstated assumptions. Yet they are not widely used in the Intelligence Community or elsewhere-possibly because current versions are cumbersome or require too much time. Crowdsourcing has the potential to solve these problems by dividing the labor, allowing dispersed groups of analysts to contribute information and ideas where they have comparative advantages. Crowdsourcing can help analysts identify and understand alternative hypotheses, arguments, and points of view. Crowdsourcing of structured techniques may facilitate rational deliberation by integrating different perspectives, so that analysis can effectively benefit from "crowd wisdom."

With this DCL, ECR invites proposals on the development, application, and extension of formal models and methodologies for STEM education research and evaluation, including methods for improving statistical modeling, qualitative modeling, measurement, replication, and learning analytics. This includes research on methodological aspects of new or existing procedures for data collection, curation, and inference in STEM education. Similarly, ECR seeks proposals related to collection of unique databases with cross-boundary value, particularly when paired with innovative developments in measurement or methodology (standard statistical modeling, qualitative research, measurement, replication and learning analytics). Proposers must demonstrate how advances in the methodology will support important theoretical insights in STEM education research or evaluation. Proposers are encouraged to explore a wide range of fundamental research projects (in the areas of quantitative, qualitative, measurement, replication, and learning analytics methodologies)

The Science of Science & Innovation Policy (SciSIP) program supports research designed to advance the scientific basis of science and innovation policy. The program funds research to develop models, analytical tools, data and metrics that can be applied in the science policy decision making process and concern the use and allocation of scarce scientific resources. For example, research proposals may develop behavioral and analytical conceptualizations, frameworks or models that have applications across the broad array of science and innovation policy challenges. Proposals also may develop methodologies to analyze science, technology and innovation data, and to usefully convey that information to a variety of audiences. Proposals that create and improve science, engineering and innovation data, including the design of new metrics and indicators, particularly proposals that demonstrate the viability of collecting and analyzing data on knowledge generation and innovation in organizations, are encouraged.

The focus of this BAA is on discovering and accelerating innovations through three broad technical approaches that contribute to DTRA overarching research, development, technology, and engineering (RDT&E) goals. Analytic investigations; Innovative capability research and development; and Mission-oriented experiments. Projects funded under this BAA will be analytic investigations, capability developments and/or experiments at Technology Readiness Levels (TRL) 2 through 6.

The Science of Science & Innovation Policy (SciSIP) program supports research designed to advance the scientific basis of science and innovation policy. Research funded by the program thus develops, improves and expands models, analytical tools, data and metrics that can be applied in the science policy decision making process. For example, research proposals may develop behavioral and analytical conceptualizations, frameworks or models that have applications across a broad array of SciSIP challenges, including the relationship between broader participation and innovation or creativity. Proposals may also develop methodologies to analyze science and technology data, and to convey the information to a variety of audiences. Researchers are also encouraged to create or improve science and engineering data, metrics and indicators reflecting current discovery, particularly proposals that demonstrate the viability of collecting and analyzing data on knowledge generation and innovation in organizations.

The National Center for Science and Engineering Statistics (NCSES) of the National Science Foundation (NSF) is one of the thirteen principal federal statistical agencies within the United States. It is responsible for the collection, acquisition, analysis, reporting and dissemination of objective, statistical data related to the science and engineering enterprise in the United States and other nations that is relevant and useful to practitioners, researchers, policymakers and the public. NCSES uses this information to prepare a number of statistical data reports as well as analytical reports including the National Science Board's biennial report, Science and Engineering (S&E) Indicators, and Women, Minorities and Persons with Disabilities in Science and Engineering. The Center would like to enhance its efforts to support analytic and methodological research in support of its surveys, and to engage in the education and training of researchers in the use of large-scale nationally representative datasets. NCSES welcomes efforts by the research community to use NCSES data for research on the science and technology enterprise, to develop improved survey methodologies for NCSES surveys, to create and improve indicators of S&T activities and resources, and strengthen methodologies to analyze and disseminate S&T statistical data. To that end, NCSES invites proposals for individual or multi-investigator research projects, doctoral dissertation improvement awards, workshops, experimental research, survey research and data collection and dissemination projects under its program for Research on the Science and Technology Enterprise: Statistics and Surveys.

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

The Operations Engineering (OE) program supports fundamental research on advanced analytical methods for improving operations in complex decision-driven environments. Analytical methods include, but are not limited to, deterministic and stochastic modeling, optimization, decision and risk analysis, data science, and simulation. Methodological research is highly encouraged but must be motivated by problems that have potential for high impact in engineering applications. Application domains of particular interest to the program arise in commercial enterprises (e.g., production/manufacturing systems and distribution of goods, delivery of services), the public sector/government (e.g., public safety and security), and public/private partnerships (e.g., health care, environment and energy). The program also welcomes operations research in new and emerging domains and addressing systemic societal or technological problems. The OE program particularly values cross-disciplinary proposals that leverage application-specific expertise with strong quantitative analysis in a decision-making context. Proposals for methodological research that are not strongly motivated by high-potential engineering applications are not appropriate for this program.

The Operations Engineering (OE) program supports fundamental research on advanced analytical methods for improving operations in complex decision-driven environments. Analytical methods include, but are not limited to, deterministic and stochastic modeling, optimization, decision and risk analysis, data science, and simulation. Methodological research is highly encouraged but must be motivated by problems that have potential for high impact in engineering applications. Application domains of particular interest to the program arise in commercial enterprises (e.g., production/manufacturing systems and distribution of goods, delivery of services), the public sector/government (e.g., public safety and security), and public/private partnerships (e.g., health care, environment and energy). The program also welcomes operations research in new and emerging domains and addressing systemic societal or technological problems. The OE program particularly values cross-disciplinary proposals that leverage application-specific expertise with strong quantitative analysis in a decision-making context. Proposals for methodological research that are not strongly motivated by high-potential engineering applications are not appropriate for this program.

This Broad Agency Announcement (BAA) seeks to provide research and development for forming a revolutionary approach to information fusion and analysis by leveraging service-oriented architecture, open standards, and cutting-edge fusion and analytical algorithms to provide real-time (or near real-time) intelligence for decision makers. This BAA shall research and develop novel techniques to assist users with discovering the golden nuggets in the data - potential approaches include fusing diverse data sources, filtering noise, and leveraging pattern learning to derive patterns of life. Further, technical capabilities developed under this BAA will minimize user time spent gathering data and reporting data, while preserving and providing more time for analysis. This will be accomplished through several means to include a data framework that can easily and quickly connect to sundry data sources, a rich, intuitive personalized workspace and experience, a variety of user-defined visualization displays, machine learning to assist and automate mundane tasks, and a custom report generation tool.

The Defense Sciences Office (DSO) at the Defense Advanced Research Projects Agency (DARPA) is soliciting innovative research proposals in the areas of: automated and sensor fused data analytics, network infrastructure and system integration, and interfaces and interoperability to support a networked system for the detection, interdiction, and deterrence of clandestine weapons of mass destruction (WMD) activities. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, and systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.

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

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

We are seeking proposals for new research that will support and enhance the activities of the Consortium for the Valuation of Applications Benefits Linked to Earth Science (VALUABLES), a new consortium at RFF made possible through a partnership with the National Aeronautics and Space Administration (NASA). VALUABLES is focused on advancing innovative uses of existing methods and developing new techniques for valuing the information provided by Earth observations, especially those derived from satellites and aircraft. Any area in which Earth observations play a role may be addressed, including applications relating to human health, air quality, water resources, ecosystem services, natural disasters, food security and agriculture, wildland fires, energy, urban development, and transportation and infrastructure. However, the use of remotely sensed data must be a key component of the analysis. In addition, we especially welcome proposals that focus on evaluating the socioeconomic impacts of applications of Earth observations for solving pressing societal problems and that quantify, in monetary terms, the value of Earth observations in specific applications. In so doing, proposals should clearly describe how information from Earth observations makes improvements to decision making and the value of those improvements. Applicants may propose to quantify the private and/or social benefits of applications of Earth observations, including nonmarket benefits. The Macauley Award is open to researchers at US-based universities and nonprofit research institutions. Interdisciplinary research teams are preferred, and teams that involve both economists and Earth scientists are particularly encouraged.

This program supports innovative research on the theories of dynamical systems, including new analytical and computational tools, as well as the novel application of dynamical systems to engineered systems.  The program is especially interested in transformative research in the area of complex systems, uncertain or stochastic nonlinear dynamical systems, model order reduction of nonlinear or infinite dimensional dynamical systems, discrete nonlinear dynamical systems, and modeling, simulation, analysis and design of multi-scale multi-physics dynamical systems. 

The ARI is a joint Domestic Nuclear Detection Office (DNDO) and National Science Foundation (NSF) program seeking novel cross-cutting research that will enhance the nation's ability to detect and interdict nuclear or radiological material outside of regulatory control, and otherwise help prevent nuclear or radiological attacks.  This year's solicitation topics will encompass a range of subjects, with an emphasis on unconventional, multidisciplinary approaches to threat detection. A number of small to medium awards are intended in the areas of novel approaches to extremely low-cost threat detection, orthogonal and informatics approaches to threat detection, deterrence analytics, and advanced forensics techniques. A single large award is intended for an integrated, multidisciplinary approach to shielded special nuclear material detection.  Primary objectives of the ARI include advancing fundamental knowledge in the above areas and developing intellectual capacity in scientific fields relevant to long-term advances in these areas. 

The Materials Engineering and Processing (MEP) program supports fundamental research addressing the interrelationship of materials processing, structure, properties and/or life-cycle performance for targeted applications. Research proposals should be driven by the performance or output of the material system relative to the targeted application(s). Research plans driven by scientific hypotheses are encouraged when suitable. Materials in bulk form or focus on special zones such as surfaces or interfaces that are to be used in structural and/or functional applications are appropriate. All material systems are of interest including polymers, metals, ceramics, semiconductors, composites and hybrids thereof. Analytical, experimental, and numerical studies are supported and collaborative proposals with industry (GOALI) are encouraged.

The purpose of this Dear Colleague Letter is to advise you about funding opportunities at the National Science Foundation for the research community to propose research projects or workshops that will gather data on the implementation and impacts of recent science policy initiatives including, but not limited to those noted earlier.  Especially encouraged are proposals that will: Develop new, or improve existing, analytical frameworks for evaluating the impacts of federal science policy initiatives; Explore different agencies' approaches to the implementation of particular policies to examine how variations in approach affect the achievement of intended policy outcomes; Collect case-study or quantitative data that facilitate identification of best practices in science and innovation policy implementation.

Computational approaches play a central role in understanding and advancing sustainability.  CyberSEES supports research on all sustainability topics that depend on advances in computational areas including optimization, modeling, simulation, prediction, and inference; large-scale data management and analytics; advanced sensing techniques; human computer interaction and social computing; infrastructure design, control and management; and intelligent systems and decision-making. Additionally, the widespread, intensive use of computing technologies also introduces sustainability challenges and motivates new approaches across the lifecycle of technology design and use.