NIST works with the National Research Council (NRC) to offer awards for postdoctoral research in the diverse fields of physical science in which NIST is actively involved. The program provides advanced training for highly qualified postdoctoral, while enhancing the research conducted in federal laboratories.
The NIST/NRC postdoctoral associateship program brings value to the federal research enterprise by inviting new ideas and skills and helping to build future generations of research leaders. For NIST positions, the NRC application process is very competitive and involves two application cycles with application deadlines of February 1 and August 1 of each year. The NRC evaluates all applications using a peer review process. Each application receives multiple reviews by scientists and engineers with expertise across the range of sponsored programs. Sponsor labs have input on the quality of applicants and the relevance to their programs. Successful applicants serve a two-year term, working directly with staff scientists
- Analysis and Theory of Microfluidic Systems
Contact: Paul Patrone
Microfluidics offers unprecedented avenues for controlling and characterizing the behavior of physical, chemical, and biological systems. However, in order to leverage the full potential of microfluidic devices as measurement tools, the community requires a deeper theoretical understanding of how they operate. Our current research addresses this problem by developing and analyzing mathematical models of such systems. We use a variety of approaches -- applied analysis, asymptotics, numerical methods, and optimization -- to understand how such models can be used as the basis for measuring properties of particles and other systems in flow. We also work closely with experimentalists in the Physical Measurement Laboratory to develop new microfluidic devices and measurement tools, and to validate our mathematical approaches.
Opportunity Number: 50.77.11.C0256
- Applied Optimization and Simulation
Contact: Anthony Kearsley
Applied optimization and simulation form an area of engineering that sits between mathematics and computer science. They include computational tools used to solve important problems in engineering, economics, and all branches of science. Current concerns include the development and analysis of algorithms for the solution of problems of estimation, simulation and control of complex systems, and their implementations on computers. We are particularly interested in nonlinear optimization problems, which involve computationally intensive function evaluations. Such problems are ubiquitous; they arise in simulations with finite elements, in making statistical estimates, or simply in dealing with functions that are very difficult to handle. The comparability among the various techniques for numerical approximation through optimization algorithms is very important. What makes one formulation for the solution of a problem more desirable than another? This work requires the study and understanding of the delicate balance between the choices of mathematical approximation, computer architecture, data structures, and other factors-a balance crucial to the solution of many applications-driven problems.
Opportunity Number: 50.77.11.B4450
- Autonomous Control of Quantum Systems
Contact: Justyna Zwolak
Machine Learning and AI are having great impacts across a number of fields of physics, from probing the evolution of galaxies to calculating quantum wave functions to discovering new states of matter. This research opportunity revolves around building machine learning-driven autonomous systems for calibration and control of quantum information science systems.
Working closely with scientists in other NIST laboratories, as well as several external collaborators, we are developing machine learning-driven autonomous systems for calibration and control of quantum information science systems. In particular, we are combining machine learning algorithms for in situ classification of quantum experimental systems (i.e., in real-time, during the experiment) with custom optimization algorithms to design an automated control protocol. The proposed protocols are implemented and validated experimentally.
The current applications of interest include, but are not limited to, tunable quantum dots and cold atom systems.
Opportunity Number: 50.77.11.C0388
- Cloud Computing and Combinatorial Software Testing
Contact: Raghu Kacker
Investigations of actual faults have shown that software failures can be triggered from certain combinations of the values of up to six variables. We have developed publicly available tools to generate test suites which assure that all t-way combinations for up to six are tested, have few test runs, and accommodate complex constraints inherent in the software under test. We are developing tools to identify faulty combinations from output of combinatorial test suites without assuming statistical models for the faults. Application domains include security, assurance of access control of health records, interoperability of systems, and assurance of modeling and simulation systems. We are investigating development of test infrastructures for cloud computing systems. It could target testing services running in the cloud or testing the cloud infrastructure or both.
Opportunity Number: 50.77.11.B7496
- Complex Systems and Networks: Performance, Control, and Security
Contact: Vladimir Marbukh
We are developing novel methodologies and approaches to modeling complex systems consisting of a large number of interacting elements. The models should not only have predictive power, but should also provide guidance for controlling complex systems. Since performance of complex systems is characterized by multiple competing criteria, which include economic efficiency, resilience, and security, the purpose of control is optimization of the corresponding trade-offs. In a situation of complex systems comprised of selfish elements, control should take advantage of market mechanisms, which elicit desirable behavior through incentives. Resilience, robustness, and security should be modeled against malicious agents attempting to cause deterioration in the system performance.
Opportunity Number: 50.77.11.B7430
- Computational Electromagnetics (Boulder, CO)
Contact: Zydrunas Gimbutas
We are developing high order integral nist-equation methods and numerical tools for computational electromagnetics. This research focuses on the frequency domain electromagnetic field solvers that involve automatic geometry preprocessing/compressing in the presence of geometric singularities and coupling the obtained discretizations to the wideband fast multipole method based accelerators and direct solvers. Applications will include benchmarking, verification, and error analysis of magnetic resonance imaging simulators and electromagnetic scattering codes.
Opportunity Number: 50.77.12.B7912
- Computational Fluid Dynamics and Mathematical Modeling
Contact: Geoffrey McFadden
Numerical and analytical methods are used to study problems that involve convection or diffusion in physics and chemistry. Of particular interest is the formulation and implementation of methods that are suitable for large-scale computations. Analysis of model problems is also pursued when appropriate.
One application is the description of convection occurring during the solidification of binary alloys. Interesting features of this problem include the existence of significantly different time scales associated with diffusion of temperature and concentration, and the behavior of the interface between the liquid and solid phases of the material.
Opportunity Number: 50.77.11.B2054
- Computational Methods for the Solution of the Time Dependent Schroedinger Equation
Contact: Barry Schneider
The solution of the time dependent Schroedinger nist-equation for many-electron atoms and molecules exposed to electromagnetic radiation presents a formidable problem both conceptually and computationally. A group of researchers at Drake University, Louisiana State University, and NIST have been developing quite sophisticated computational approaches to treating "small" atomic and molecular systems exposed to short, intense laser radiation. Extracting quantitative results has necessitated large-scale calculations on supercomputers. The methods developed are state-of-the-art and the computer codes have been algorithmically designed to scale efficiently to many thousands of processors. They have been applied to a number of one, two, and many electron atoms and molecules to extract single and double ionization probabilities. To date, the calculations have revealed numerous interesting and unexpected features, in single and double ionization processes that are among the first of their kind.
We are interested in expanding the scope of our work in several ways. In order to treat larger molecular systems, new approaches are required. These include things such as developing more efficient hybrid basis sets adapted to treat large molecules, new time propagation algorithms and density-functional-based methods that are needed to quantitatively model dynamical processes in very large molecular systems. Of particular interest are reformulations DFT to explicitly remove self-interaction errors and extending these functionals to the strong-field, time-dependent domain.
The group currently has a number of NSF and DOE awards and has successfully competed for computational resources on the eXtreme Science and Engineering Discovery Environment project. An Associate joining the project will have access to the most sophisticated and powerful computers in the world and will also get to collaborate with a world class group of theoretical and computational physicists.
Opportunity Number: 50.77.11.B8188
- Dynamical Systems and Computational Biology
Contact: Fern Hunt
Our research projects are concerned with the application of stochastic processes in nonlinear dynamical systems and computational biology. Data from complex physical and biological systems present challenges to conventional modeling and statistical techniques. The goal is to apply recent theoretical advances in probability and dynamical systems to areas relevant to NIST’s mission. We are currently studying dynamical systems that arise from the control of computer networks, as well as the emergence of patterned behavior and aggregation in complex networks.
Opportunity Number: 50.77.11.B6285
- Immersive Visualization
Contact: Judith Terrill
With the continuing increase in speed and capability of commodity graphics processors, immersive visualization offers increasing opportunities to express scientifically meaningful results. Data at NIST spans a wide range from nano to cement to models that exhibit complex dynamics. This research will build on our open source software that runs on a linux desktop as well as immersively. Opportunities exist for (1) investigating the use of immersive visualization as a scientific instrument for exploration and representation of data; (2) developing interactive measurement techniques on visualizations; (3) developing ways to merge analysis with visualization and provide quantitative feedback into the visualization; (4) exploring and expressing uncertainties due to the data as well as the visualization methods; (5) harnessing the growing capability of graphics processors to provide insight; (6) advancing the use of abstraction to express meaning in data; and (7) developing user interaction methods, including direct manipulation techniques.
Opportunity Number: 50.77.11.B6663
- Mathematical Modeling
Contact: Geoffrey McFadden
Working closely with scientists in other NIST laboratories, we formulate large-scale but computationally feasible models, develop efficient computer programs, and validate our simulations by comparison with experimental results. This position requires knowledge of analytical and numerical methods and application areas. It is suitable for candidates whose interest is more in mathematical modeling than in the specific application. Candidates with backgrounds in applied mathematics, engineering, physics, and materials science are encouraged to suggest a specific project.
Opportunity Number: 50.77.11.B2044
- Mathematical Modeling of Magnetic Systems
Contact: Michael Donahue
We work with scientists in other NIST laboratories to develop tools for computer simulation and analysis of magnetic systems at the nanometer scale. Model verification is achieved by comparison against experiment and by development of reference problems. Important issues include controlling round-off and truncation error to obtain high accuracy solutions in complex, large scale simulations, and the development towards this end of efficient, highly parallel software running on commodity hardware. Novel methods to compute the stray field from magnetized material with attention to interface and boundary effects are of particular interest. Applications include MRAM, field sensors, and magnetic logic devices.
Opportunity Number: 50.77.11.B4449
- Modeling Complex Microstructures
Contact: Stephen Langer
We are developing object-oriented computational tools for the analysis of materials with complex microstructures. Starting from a digitized micrograph, the program identifies features in the image, assigns material properties to them, generates a finite element mesh, and performs virtual measurements. The goal is to build a computational platform that can predict the macroscopic behavior of a material from knowledge of its microscopic geometry, incorporating a wide variety of physical phenomena and using a modular structure that allows new physics to be added easily.
More information is available at http://www.ctcms.nist.gov/oof/. Opportunities exist in image analysis, materials science, physics, and computer science.
Opportunity Number: 50.77.11.B8287
- Orthogonal Polynomials, Special Functions, and Digital Repositories
Contact: Howard Cohl
We conduct research in mathematics and computer science related to orthogonal polynomials and special functions. In the mathematics area, we investigate properties of the special functions of applied mathematics, especially series representations, definite integrals, and/or q-analogs of such expressions. In addition, we develop infinite series and definite integral expansions of fundamental solutions for linear elliptic partial differential nist-equations on Riemannian manifolds of constant curvature (and/or their generalizations, the rank on symmetric spaces). Research in computer science includes the development of the software infrastructure to support the NIST online Digital Repository of Mathematical Formulae.
Opportunity Number: 50.77.11.B8087
- Parallel and Distributed Computing Algorithms and Environments
Contact: William George
As the size and computational power of parallel and distributed computing systems increase, it is important to continually investigate the appropriateness of the algorithms we use for our scientific applications. Although we always strive to design and build scalable parallel applications, we must re-think these deigns when the available computational resources increase in power by even as small as a single order of magnitude with respect to the number of processors, main memory size, network speed, or other relevant parameters. This research opportunity focuses on (1) investigating and developing new parallel algorithms, especially for scientific applications, for the next generation of computing platforms; (2) characterizing the programming models presented by new parallel and distributed computing platforms; (3) investigating the design and performance of parallel programming languages and libraries; and (4) investigating the role of web services, fourth generation languages such as Matlab and Mathematica, computational grids, and other developing technologies in providing novel high-performance computing environments.
Opportunity Number: 50.77.11.B6377
- Quantum communication
Contact: Xiao Tang
Transmitting quantum states of single photons from one location to another is one of the most important routines of any quantum communication system. Our current research areas in quantum communication include single photon creation, transmission, storage, transduction and detection - these are the fundamental building blocks for the quantum communication systems of the future. In such systems, one will encode information into strategically created single photons (flying Qubits), transmit them and interface them with an atomic quantum memory (stationary Qubits) for storage and processing. A typical example of such systems are quantum repeaters that can extend operation distance for quantum key distribution (QKD) systems, and/or connect quantum computers to form the large-scale quantum networks of the future. Our current research is focused on the following areas: (1) Entangled photon pair sources: Based on parametric frequency down conversion or four-wave mixing, single photon pairs are generated in a resonant cavity to ensure the linewidth is sufficiently narrow to match the atomic systems for high efficiency; (2) Quantum memories: Our current approach is based on electromagnetically induced transparency (EIT) using warm atomic vapor or cold atomic ensamples in a magneto-optical trap (MOT); (3) Quantum interface: By using nonlinear optical materials, wavelengths of the generated single photons can be converted between atomic transition lines for storage and processing and telecommunication bands for long distance transmission; (4) Bell state measurement: The measurement results provide information for quantum teleportation and; (5) Electronics and software for final integration of a quantum repeater.
Opportunity Number: 50.77.11.B6541
- Quantum Frequency Conversion for Hybrid Quantum Networks
Contact: Paulina Kuo
Future quantum networks will consist of a mixture of different technologies that operate at different wavelengths. Our research focuses on photonic devices that can bridge these different wavelengths while maintaining the quantum properties. We study quantum frequency conversion (QFC) using high efficiency, nonlinear optical frequency conversion (sum- and difference-frequency generation). We are interested in properties of QFC devices, such as efficiency, noise, and bandwidth, and potential integration of QFC devices with qubit technologies.
Opportunity Number: 50.77.11.B8345
- Quantum Information and Cryptography
Contact: Yi-Kai Liu
Quantum mechanical devices can perform certain information processing tasks that are impossible using only classical physics. However, the construction of such devices requires new ideas from computer science, mathematics, and physics. We are interested in a broad range of topics in this area, including quantum devices that implement novel cryptographic functionalities, methods for testing and characterizing experimental quantum information processors, and classical cryptosystems that are secure against quantum adversaries. We are also interested in related areas such as quantum algorithms, complexity theory, and machine learning.
Opportunity Number: 50.77.11.B7913
- Quantum Information Science (Boulder, CO)
Contact: Emanuel Knill
Quantum information science covers the theoretical and experimental areas involving the use of quantum mechanics in communication and computation. We are particularly interested in benchmarking proposed physical system’s performance on quantum information processing tasks, scalably realizing logical qubits, and developing algorithms that take advantage of quantum resources. The research is inspired by and will contribute to the technologies being developed at NIST.
Opportunity Number: 50.77.12.B5623
- Quantum Networking (Boulder, CO)
Contact: Emanuel Knill
Distributed quantum computing requires quantum networks that can carry flying qubits. Such networks can be used to scale up small quantum computers and enable quantum communication protocols such as blind quantum computing for certified execution of quantum algorithms. This project involves a joint theoretical-experimental effort to develop and test quantum networking infrastructure, protocols and devices to convert computational qubits such as superconducting and electrically defined quantum dot qubits to flying qubits. This opportunity is for the theoretical component of the project.
Opportunity Number: 50.77.12.C0180
- Real-time Quantitative Visualization
Contact: Judith Terrill
We are working to create visualization systems that serve as precision measurement instruments, supporting interactive probing of “samples” to derive quantitative data to enable scientific discovery. We use virtual samples, built from data obtained from either physical measurement or computational simulation.
Our ability to extend measurement science to the virtual world is enabled by advances in the speed and capability of graphics processing units (GPUs). In particular, visualization techniques that employ shaders have the potential to play a central role in measurement and analysis tools within a visualization system because these programs can perform substantial numeric processing within the visualization pipeline where they have direct access to the geometric data describing the objects of study.
Additionally, this allows access to the information needed to determine uncertainties, a prerequisite for precision measurement. This research opportunity focuses all aspects of quantitative visualization, i.e., measurement and analysis applied to visualization objects directly in real-time.
Opportunity Number: 50.77.11.B7763
- Scientific Datamining
Contact: Judith Terrill
NIST scientists are currently automating experiments resulting in increasing amounts of generated data in multidimensional spaces. The data come primarily from combinatorial experiments in materials science. This type of data consists of image data with additional measurements at each pixel. Other experiments result in spectra-like measurements taken over spatial domains. These datasets require techniques that can sift through large amounts of data for items of potential interest, as well as for discovery. We are collaborating with these scientists on ways to mine this data for scientific insight. Opportunities exist for the application of datamining techniques such as classification, rule finding, and automated model building to these datasets, as well as for the development of new techniques.
Opportunity Number: 50.77.11.B4825
- Standardizing of Measurements on Medical Images
Contact: Judith Terrill
NIST scientists are applying measurement science to medical images of lung tumors. The change in pulmonary nodules over time is an extremely important indicator of tumor malignancy and rate of growth. With current technology, tumor sizes, from which changes in size over time are calculated, are measured through computed tomography (CT), though often on different CT machines, with different operators, at different times of the day, and with patients in different physical positions relative to the CT equipment. Our long-term goal is to be able to make lung tumor measurements that are independent of these operating conditions. We are working on two projects to achieve this goal: (1) developing a volumetric measurement technique that is completely automated, independent of any user input parameters; and (2) creating standardized lung tumor data sets to test measurement techniques. For the latter, our approach is to embed known geometric objects into clinical lung tumor data, taking into account the noise of the data and the error involved with the gridded data. We will recreate the complications that arise in clinical tumor measurements by embedding synthetic tumors into areas of high vasculature and onto the pleural lung linings to use as standards to compare measurement techniques.
Opportunity Number: 50.77.11.B7281
- Statistics for Quantum Systems (Boulder, CO)
Contact: Scott Glancy and Emanuel Knill
Sophisticated, rigorous statistical tools are required to analyze data from experiments that manipulate and measure quantum systems with the goals of quantum computation, communication, and measurement. This project works to develop new methods for data analysis from quantum experiments. Particular applications of interest include quantum state and process tomography, certifying violation of local realism (e.g., in Bell tests), certification/quantification of randomness, and use of quantum resources to improve measurement precision. We work in close collaboration with experimental groups at NIST (trapped ions, superconducting qubits, photons) to assist experiment design and analysis and to inspire new theoretical research.
Opportunity Number: 50.77.12.B7973
- Stochastic Modeling, Verification, Validation, and Calibration of Computer Simulations
Contact: Jeffrey Fong
Simulations of high-consequence engineering, physical, chemical, and biological systems depend on complex mathematical models. Such models may include large number of variables, parameters with uncertainties, incomplete physical principles, and imperfect methods of numerical solution. To ensure the public that decisions made on the basis of such models are well founded, rigorous techniques for verification and validation of computer simulations must be developed. Techniques under investigation include stochastic modeling, metrology-based error analysis, standard reference benchmarks and protocols, design of physical and numerical experiments, and uncertainty analysis of finite element method. We are also interested in applications to specific engineering, physical, chemical, and biological systems of technological importance; and basic research in continuum physics, irreversible non-equilibrium thermodynamics, nonlinear viscoplasticity theory, fatigue, fracture, and damage mechanics; fire-structure dynamics; microelectromechanical (MEM) systems; nanoscale contact mechanics; cochlear mechanics of human inner ear; and stability of stochastic elastic, viscoelastic, and viscoplastic systems.
Opportunity Number: 50.77.11.B6328
- Uncertainty Quantification and Computational Materials Science (Boulder, CO)
Contact: Andrew Dienstfrey
We research and develop mathematical and statistical analysis and tools for uncertainty quantification in scientific computing, with particular emphasis on problems in computational material science. Application areas include, but are not limited to structural composites and electronic materials. This work, which is performed in collaboration with the NIST Material Measurement Laboratory, is in response to the multi-agency Materials Genome Initiative (http://www.whitehouse.gov/mgi/), which strives to reduce the time and costs for materials discovery, optimization, and deployment through the promotion of a new research and development paradigm in which computational modeling, simulation, and analysis will decrease the reliance on physical experimentation.
Opportunity Number: 50.77.12.B7897
- Validated Computation of Special Functions: DLMF Standard Reference Tables on Demand
Contact: Bonita Saunders
We are developing an online system for generating validated tables of special function values with an error certification computed to user-specified precision. A typical user might be a researcher or software developer testing his own code or confirming the accuracy of results obtained from a commercial or publicly available package. The goal is to create a standalone system, but also link to and from the NIST Digital Library of Mathematical Functions (DLMF).
The project, DLMF Standard Reference Tables on Demand (DLMF Tables), is a collaborative effort with the University of Antwerp Computational Mathematics Research Group (CMA) led by Annie Cuyt. A beta site based on CMA’s MpIeee, a multiprecision IEEE 754/854 compliant C++ floating point arithmetic library, is already available at http://dlmftables.uantwerpen.be/. The successful candidate will have the opportunity to advance our current efforts in the field of validated computing through the continued research and development of multiple precision function software providing guaranteed error bounds at arbitrary precision. The associate will also help expand DLMF Tables into a full-fledged site, as well as investigate the enhancement of existing multiprecision libraries for possible inclusion in DLMF Tables.
Opportunity Number: 50.77.11.C0297
- Cognitive Radio Networks
Contact: Nader Moayeri
Our research has focused on opportunistic spectrum access (OSA) in the context of cognitive radio networks (CRNs). We have been interested in development and performance evaluation of methods for detection of primary user (PU) activity whether by a single secondary user (SU) or through collaboration among a number of such users. We have also worked on developing and optimizing OSA mechanisms that maximize SU throughput subject to an upper bound on the interference caused for PUs using the ROC curves characterizing the performance of the PU detection scheme. Along the same lines, we are interested in exploiting location information for OSA as well as developing pricing mechanisms that regulate OSA and coexistence of PUs and SUs.
More generally, we are interested in any aspect of OSA and CRNs including but not limited to power control, MAC layer protocols, quality of service (QoS), and routing.
Opportunity Number: 50.77.21.B7381
- Localization and Tracking
Contact: Nader Moayeri
NIST has had extensive research activities in the area of indoor localization and tracking. We are interested in any method for solving this challenging problem, whether through use of RF-based techniques, inertial navigation, or other means such as ultrasound, altimeters. We are interested in not only improving the location accuracy of localization methods, but also doing so in such a way that the resulting systems would not be too expensive or difficult to use. Other areas of interest include cooperative localization and hybrid localization methods.
Location information is expected to play an important role in smart devices and the Internet of things in the future. Thus, NIST is also interested in location-based services (LBS).
Opportunity Number: 50.77.21.B7382
- Network Function Virtualization and Software Defined Networking
Contact: Kotikalapudi Sriram
We seek researchers in network virtualization, network service function chaining, software defined networks, technologies, and techniques to address robustness safety and security of virtualized network services, novel applications of NFV/SDN to domains such as network security and intrusion detection, support of cyber physical systems communications, support of advanced mobility and cloud computing.
Opportunity Number: 50.77.21.B8054
- Next Generation Internet Architectures
Contact: Kotikalapudi Sriram
We seek researchers in next generation network architectures, including fundamentally new approaches to content delivery, service architectures, management and control, security and privacy, disruption tolerant networking, and handling of mobility. We have particular interest in measurement and modeling techniques to enable quantitative comparisons between significant NGIA proposals emerging from the academic research community. Specific desirable topics include Internet scale simulation & emulation modeling, Information Centric Networking (ICN) and Industrial Internet architectures.
Opportunity Number: 50.77.21.B8053
- Predicting Global Failure Regimes in Complex Information Systems
Contact: Kevin Mills
Over the past five years, we investigated methods to characterize global behavior in large distributed systems and applied those methods to predict effects from deploying alternate distributed control algorithms (a complete record of this research is available at http://www.nist.gov/itl/antd/emergent_behavior.cfm). The methods we used assess global behaviors under a wide range of conditions, enable significant understanding of overall system dynamics, and yield insightful comparisons of competing control regimes. On the other hand, such methods do not provide information about potential for rare combinations of events to drive system dynamics into global failure regimes, leading to catastrophic collapse. Our ongoing research aims to address this topic using two complementary thrusts: (1) design-time methods that enable system architects to identify and evaluate global failure scenarios that could lead to system collapse and (2) run-time methods that alert system operators about incipient transition to global failure regimes, and subsequent collapse. Effective design-time methods will enable architects to devise mechanisms that can prevent high-risk scenarios. Since no design-time methods can identify all possible failure scenarios, effective run-time methods will signal operators when system trajectory trends toward collapse, allowing remedial actions to forestall or mitigate catastrophic failure. We seek research collaborators who are also interested in design-time and run-time methods to predict global failure regimes in infrastructures on which modern society increasingly depends.
Opportunity Number: 50.77.21.B8242
- Robust Inter-Domain Routing
Contact: Kotikalapudi Sriram
We seek researchers in global Internet routing security and robustness, Border Gateway Protocol (BGP), measurement monitoring and analysis of global BGP behavior, BGP security, and BGP scalability and performance issues, routing anomaly detection, and next generation routing architectures.
Opportunity Number: 50.77.21.B8052
- Smart Network Metrology and Analytics
Contact: Yang Guo
Network traffic measurement provides the basis for many applications, ranging from network management to accounting and security; yet the current state-of-the-art in network metrology is inadequate, providing surprisingly little visibility into detailed network behaviors and often requiring significant manual intervention to operate. Such practice becomes increasingly ineffective as the networks grow both in size and complexity. To make matters worse, even when the fine-grained measurement is available, analyzing vast amounts of raw data poses a major computational challenge. Useful features are often buried in noisy data and meaningful analysis often requires reducing the feature space to make the analysis more computationally feasible. Furthermore, processing delays make it difficult to react to possible security attacks and network management issues in a timely fashion.
We seek capable candidates who are interested in joining our ongoing effort on developing Machine-Learning Directed Programmable Network Metrology, which supports multi-scale, pervasive, and dynamically tunable network measurement and analytics in Software Defined Networks (SDN). As an Associate, you can contribute both systematically (by participating in the system development) and analytically (by developing Machine Learning based algorithms). You are encouraged to contact us directly for more information regarding this opportunity.
Opportunity Number: 50.77.21.B8517
- Synthetic Reference Materials for Scalable Cyber Security
Contact: Kevin Mills
We aim to develop methods and tools to generate high-fidelity, purely synthetic, network traces that maximally approximate the diversity of enterprise network traffic and that can be instrumented with controlled instances of malicious traffic. We will apply the resulting tools as a foundation for measurement-based approaches to evaluate the effectiveness of network anomaly detection systems. We seek research collaborators in any of six areas: (1) statistical modeling of enterprise network traffic based on real traffic traces; (2) modeling of malicious traffic, including attacks and anomalies; (3) generation of synthetic traffic traces that approximate the characteristics of real traffic traces; (4) generation of synthetic traffic traces that include controlled injection of malicious and anomalous traffic; (5) statistical techniques to assess the fidelity of synthetic traffic traces when compared against real traffic; and (6) development of methods to evaluate the effectiveness of network anomaly detection systems.
Opportunity Number: 50.77.21.B8043
- Statistical Methods for Extreme Values
Contact: Adam Pintar
The analysis of extreme values is important in many areas of application. One concern of the Structures Group at NIST is designing structures, especially tall ones, to withstand extreme wind events. These engineers are typically interested in estimating return values or the distribution of the maximum or minimum value from univariate and possibly multivariate time histories, collected outdoors and from wind tunnel tests. Novel statistical methods for these kinds of data could help in designing more cost-efficient structures that remain safe.
Reference: Pintar AL, Simiu E, Lombardo FT, Levitan M: "Maps of Non-tornadic Wind Speeds With Specified Mean Recurrence Intervals for the Contiguous United States Using a Two-dimensional Poisson Process Extreme Value Model and Local Regression". NIST Special Publication 500-301 http://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.500-301.pdf
Opportunity Number: 50.77.61.C0058
5G Wireless Communications Science–Statistical Modeling, Metrology, and Uncertainty (Boulder, CO)
Contact: Michael Frey
Fifth generation (5G) wireless is a suite of communication technologies that together can achieve data rates and latencies comparable to installed optical fiber. 5G wireless is expected to be an essential component of national and global infrastructure in the coming decades, enabling new modes of communication, computing, and sensing and underpinning future robust autonomous control, delivery, and transportation systems. Key elements of 5G wireless technology open for study include multiple-input, multiple-output (MIMO) antenna designs, dense networks, characteristics of and exploitation of “millimeter wave” spectrum (frequencies above 24 GHz), and, broadly, more effective, efficient (cognitive) spectral usage.
Effective development and deployment of 5G technologies depends on reliable, repeatable measurement of their physical characteristics for benchmarking and standards development. The National Advanced Spectrum and Communications Test Network (NASCTN) at the Boulder, Colorado campus of the National Institute of Standards and Technology (NIST) is a leading metrology center for advanced communications. NASCTN has available to it the full resources of NIST and is committed to all aspects of scientific understanding and innovative development and deployment of 5G wireless technologies.
NASCTN activities present statistical research opportunities along two mutually supporting tracks. First, communication is fundamentally statistical by nature, and this perspective necessarily underpins NASCTN studies of the theoretical performance of any of 5G technology’s key elements. These studies draw on the full scope of statistical theory from inference to information theory and offer many avenues for investigation. Second, NASCTN measurement efforts present interesting challenges for experimental design and for related statistical modeling and uncertainty analysis. Statistical research is invited to both support modeling and analysis of 5G theoretical performance and address metrological experimental design and analysis challenges.
Opportunity Number: 50.77.62.C0083
Statistical baseline spectrum estimation and uncertainty analysis (Boulder, CO)
Contact: Michael Frey
Statistical estimation of physical spectra is central to many laboratory processes, including, prominently, atom probe tomography, electromagnetic radiation characterization, gas phase spectroscopy, vibrational spectroscopy, chromatography, and NMR experiments. The baselines of measured spectra often include spectra noise, drift, and distortion, all of which obscure spectral lines, peaks, shoulders, and other spectral features of main experimental interest. Indeed, baseline distortions can be greater than and potentially fully obscure even peak spectral intensities.
In common practice, spectral baseline noise and distortion are identified and removed manually by a laboratory analyst. This is time-intensive and judgment-dependent, and the analyst’s subjective intervention greatly complicates the possibility of a subsequent rigorous uncertainty analysis. Ideally, an effective, robust automatic baseline detection/correction procedure would be available that permits statistical uncertainty estimation. A variety of such (semi-)automatic procedures have been proposed for baseline spectrum estimation, based variously on wavelets, penalized least squares, smoothers, and iteratively reweighted quantile regression. The challenge remains to adapt one or a combination of these procedures or to devise new procedures, perhaps machine learning-based, that (1) applies broadly to different physical spectral data without user intervention, (2) capably recognizes different forms of baseline corruption, and (3) allows a well-founded uncertainty analysis. Progress on these problems is an exciting technical challenge with, potentially, significant benefit to many laboratory sciences.
Opportunity Number: 50.77.62.C0081