ACMD Postdoctoral Opportunities
This document describes National Research Council Postdoctoral Research Associateships tenable within the Applied and Computational Mathematics Division (ACMD) of the NIST Information Technology Laboratory. Research areas of interest include computational materials science, computational electromagnetics, computational biology, computational
chemistry, orthogonal polynomials and special functions, applied optimization and simulation, complex systems and networks, data mining, scientific visualization, parallel and distributed algorithms, and quantum information science. See below for more details on these opportunities and the research advisors associated with them. Candidates are urged to contact potential advisors in advance of formal application. Candidates and their research proposals are evaluated in a competitive process managed by the National Research Council (NRC) Associateship Programs. For further details on the application process, see the link below. For further information on the program within ACMD, contact Ronald Boisvert (boisvert [at] nist.gov (boisvert[at]nist[dot]gov)).
This opening in Special Functions is connected with a multidisciplinary program of research and development that focuses on functions that have recognized or potential importance in scientific applications. Research proposals relating to mathematical analysis and computer science in the area of orthogonal polynomials and special functions will be considered.
Opportunity Number: 50.77.11.B8087
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.
NIST Digital Library of Mathematical Functions. http://dlmf.nist.gov/, Release 1.0.22 of 2019-03-15. F. W. J. Olver, A. B. Olde Daalhuis, D. W. Lozier, B. I. Schneider, R. F. Boisvert, C. W. Clark, B. R. Miller, and B. V. Saunders, eds.
B. I. Schneider, B. R. Miller, and B. V. Saunders. NIST’s Digital Library of Mathematical Functions. Physics Today 71:2 (2018), 48. DOI: 10.1063/PT.3.3846.>
F. Backeljauw, S. Becuwe, A. Cuyt, J. Van Deun, and D. Lozier. Validated Evaluation of Special Mathematical Functions. Science of Computer Programming 90 (2014), 2-20. https://doi.org/10.1016/j.scico.2013.05.006.
Opportunity Number: 50.77.11.C0297
Contact: Mike 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
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 application-driven problems.
Opportunity Number: 50.77.11.B4450
Contact: Ryan Evans
Mathematical modeling forms the basis for understanding, simulating, optimizing and controlling numerous scientific phenomenon and associated measurements. Mathematical models frequently take the form of an ordinary or partial differential equations, and are often nonlinear. There are a few instances where analytic solutions are available and in other cases solutions can be sought through the design of efficient and accurate numerical solutions. Analytic solutions to similar problems or numerical approximations can be used to validate mathematical models through comparison with physical experiments, measured quantities of interest, and optimized experimental design. Methods that yield analytic solutions are often employed to reduce the complexity of mathematical models and can, in turn help describe and improve instrument behavior in physically relevant limits. Abundant examples are found in physics, chemistry, and biology. We are interested developing new mathematical models to simulate scientific experiments that produce measurements. This research often leads to numerical implementations and associated testing, to numerical analysis of accuracy, to optimization, optimal control or optimal design of instrument performance.
Opportunity Number: 50.77.11.C0761
Contact: Stephen Langer
We are developing object-oriented computational tools for the analysis of material microstructure. The goal is to predict the macroscopic behavior of a material from knowledge of its microscopic geometry. 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 to determine the effect of the microstructure on the macroscopic properties of the system. 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.B4451
Contact: Raghu Kacker
A measurement is the estimated value of a quantity plus a quantitative estimate of its uncertainty. A "virtual measurement" is a measurement produced by computation or simulation. Thus, the goal of this project is to determine the uncertainties associated with predictions from quantum chemistry calculations. Current work focuses on scaling factors, with associated uncertainties, for vibrational frequencies from ab initio and density-functional calculations. For fundamental vibrational frequencies and zero-point energies this has been completed. Scaling factors for anharmonic fundamental frequencies are now being developed. Future work will address predictions of thermochemical quantities such as entropies and heat capacities, which depend on the vibrational partition function. Alternatives to traditional frequency scaling will also be investigated.
Opportunity Number: 50.63.21.B6751
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. 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; nanoscale contact mechanics; cochlear mechanics of human inner ear; and stability of stochastic elastic, viscoelastic, and viscoplastic systems.
Opportunity Number: 50.77.11.B6328
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
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
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
Contact: Victor Albert
Error correction is what ensures that the audio in your phone calls remains sharp, your hard drives do not deteriorate too quickly, and signals can be reliably transmitted to remote satellites. Over multiple decades, and with the explosion of the information age, an enormous variety of error-correction schemes were developed. Recently, a radically new type of error correction was introduced, one that can protect the quantum information that is stored in a quantum computer or that is communicated over a quantum network. We perform state-of-the-art research in the theory quantum error correction and related topics, as well as maintain the error-correction zoo --- a repository of several hundred classical and quantum codes.
Opportunity Number: 50.77.11.C0764
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
Optical quantum metrology encompasses the generation, detection, characterization, and verification of quantum states of light, including the use of quantum entanglement to enhance sensitivity compared to classical methods. The project develops cutting-edge single-photon sources and uses state-of-the-art (photon-number-resolving) single-photon detectors to investigate fundamental aspects of optical quantum metrology. Further, the project focusses on the implementation of newly developed measurement protocols and characterization tools for single-photon sources, detectors, and components. These tools and protocols are used for applications such as entanglement distribution in quantum networks, synchronization of distant quantum network nodes, quantum component characterization and quantum-enhanced measurements using exotic and non-Gaussian quantum states of light.
Opportunity Number: 50.77.12.C0669
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.
 S. S. Kalantre, J. P. Zwolak et al. Machine learning techniques for state recognition and auto-tuning in quantum dots. npj Quantum Inf. 5 (6): 1–10 (2019).
 J.P. Zwolak, T. McJunkin et al. Auto-tuning of double dot devices in situ with machine learning. arXiv:1909.08030 (2019).
Opportunity Number: 50.77.11.C0388
Contact: Barry Schneider
The solution of the time dependent Schroedinger 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, the Technical University of Vienna, the 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 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, 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. Dynamical processes of specific interest include spin-dependent electronic rearrangements and their impact on technologically interesting collective phenomena, such as magnetic behaviors, in these systems.
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
Contact: Barry Schneider
A small group of scientists in the Information Technology and Materials Research Laboratories have been been applying neural networks to examining a number of problems in chemical physics. One problem, the Kovats retention indices used in gas chromatography, has already been successfully attacked using these approaches (Predicting Kovats Retention Indices Using Graph Neural Networks ). We have achieved an almost fourfold increase in predictive capabilities of our model based on graph neural networks over previous atom additivity approaches. We are eager to extend these ideas more broadly to predicting mass spectra, and the positions and intensities of IR spectral lines. The work has immediate application to the identification of unknown compounds of interest to the larger industrial community.
Opportunity Number: 50.77.11.C0578
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
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 on all aspects of quantitative visualization, i.e., measurement and analysis applied to visualization objects directly in real time.
Opportunity Number: 50.77.11.B7763
The effective use of high-performance computing (HPC) resources, from large CPU clusters to Leadership-Class Computing Facilities (LCCF), is key to accelerating scientific advances in many fields. This includes many of the research areas of interest to NIST such as computational material science, electromagnetics, biology, quantum information science, and others. For this research opportunity we are looking for a Computer Scientist to focus on the development and use of a parallel and distributed scientific Leadership-Class HPC application. This includes research and development in all aspects of HPC such as scalable parallel algorithms, distributed algorithms, and parallel I/O, with results post-processed for input to available analysis and visualization software, all designed to run efficiently on emerging exascale LCCF resources.
Opportunity Number: 50.77.11.B6377
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
Contact: E. (Manny) 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
Contact: E. (Manny) 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
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
Contact: A. 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
Contact: Zydrunas Gimbutas
We are developing high order integral 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