ACMD Postdoctoral OpportunitiesThis document describes National Research Council Postdoctoral Research Associateships tenable within the Applied and Computational Mathematics Division of the NIST Information Technology Laboratory.
NIST NRC Research Associateship Program
Joint NIST-NIH NRC Research Associateship Program
ACMD Research Topics and Advisors Opportunities available under the Joint-NIST-NIH Program are explicitly identified below. The remainder are available under the regular NIST program.
Gaithersburg, MD (Joint NIST-NIH Program)
Gaithersburg, MD
Contact: Geoffrey B. 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
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
Special Functions Contact: Daniel W. Lozier We are conducting a multidisciplinary program of research and development in special functions, focusing on functions that have recognized or potential importance in scientific applications. Scope includes, but is not limited to the functions that are covered in the NIST Digital Library of Mathematical Functions (http://dlmf.nist.gov). Research opportunities exist in (1) mathematical analysis, for example in asymptotics; (2) numerical and symbolic algorithms; (3) error analysis in numerical computations, especially error bounding; (4) numerical and symbolic software; (5) comparative analysis of available software; (6) software testing methodology; and (7) Web services in support of software comparison and testing. Opportunity Number: 50.77.11.B2053
Computational Fluid Dynamics and Mathematical Modeling Contact: Geoffrey B. 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
Parallel Adaptive Finite Elements Contact: William F. Mitchell This project focuses on the development and testing of new methods for the fast numerical solution of partial differential equations. We are interested in adaptive grid refinement techniques, hp-adaptive refinement, and reliable a posteriori error estimators for finite elements. We are also developing optimal order multigrid solvers and preconditioners. A primary interest is the effective implementation of these methods on modern high performance computer architectures, such as clusters of multi-core nodes. Opportunity Number: 50.77.11.B7161
Combinatorial and Discrete Algorithms Contact: Isabel Beichl We combine prababilistic methods with combinatorics to solve problems in the physical sciences, which can be formulated as combinatorial counting questions on graphs. We have devised novel formulations of statistical techniques such as importance sampling and Monte Carlo time that can be applied to these graph problems. We plan to extend these techniques to other fundamental problems related to measurement science and optimization of communications. Opportunity Number: 50.77.11.B5288
Object-Oriented Numerical Software Contact: Roldan Pozo We are performing research in the design of object-oriented scientific software in C++, C#, Java, and Python programming languages. Our goal is to produce mathematical software that allows expression of algorithms at a high level of abstraction, resulting in code that is easy to understand, use, and maintain, while obtaining competitive levels of efficiency on high-performance computer architectures. Recent work has focused on the development of software packages for numerical linear algebra, as well as software tools for the analysis of complex systems (network graphs). Opportunity Number: 50.77.11.B5022
Mathematical Modeling of Magnetic Systems Contact: Michael J. Donahue We work with scientists in other NIST laboratories to develop computer simulation and analysis of magnetic systems. Model verification is achieved by comparison against experiment and by development of standard problems. This work includes development of public domain software for reference and research. Research focuses on micromagnetic modeling. Opportunity Number: 50.77.11.B4449
Applied Optimization and Simulation Contact: Anthony J. 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
Finite Element Analysis of Material Microstructure 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
Standardizing of Measurements on Medical Images Contact: Judith E. 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
Virtual Measurements from Quantum Chemistry Contact: Raghu N. 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
Stochastic Modeling, Verification, Validation, and Calibration of Computer Simulations Contact: Jeffrey T. 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
Complex Systems and Networks: Performance, Control, and Security Contact: V. 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
Cloud Computing and Combinatorial Software Testing Contact: R. 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
Algorithm Development for Modeling Communication Systems Contact: Bradley K. Alpert Problems arising in modeling communication systems, including antennas, microwave guides, and opto-electronic devices, are attacked with tools from scientific computation, numerical analysis, and applied mathematics. Particular emphasis is placed on high-order convergent and reduced computational complexity techniques for numerical solution of integral equations and partial differential equations, including quadrature methods, fast multipole methods, and wavelet methods. Opportunity Number: 50.77.12.B3967
Algorithm Development for Sparse Recovery and Imaging Contact: Bradley K. Alpert Many measurement settings involve the recovery of models from noisy data, where the model space may be much higher dimensional than the measurements (also numerous). For example, the recovery of piecewise smooth functions from Fourier data remains an open problem. Recent progress in compressive sampling and sparse recovery suggest that a subset of these problems, which arise in spectroscopy, tomography, and magnetic resonance imaging, may be amenable to solution. Opportunity Number: 50.77.12.B7282
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
Gaithersburg, MD (Joint NIST-NIH Program)
Simulation of Bioregulatory Networks Involved in Cell Cycle Control (Joint NIST-NIH Program) Contact: Geoffrey B. McFadden (Co-Adviser at NIH: M.I. Aladjem) Proper cell growth depends on a network of interacting molecules that monitors cellular metabolism and environmental signals. This network ensures that cells halt their growth in response to unfavorable conditions such as the absence of sufficient nutrients or the presence of potentially damaging agents. When cells escape these controls, the results are developmental abnormalities, genomic instability, and cancer. Although knowledge about the individual molecules that regulate cell growth has increased exponentially in recent years, our ability to make sense of this detailed information has not. To understand how signals are transmitted through the network, we need to integrate molecular data in a clear, standardized, computer-readable format. NIH's Laboratory of Molecular Pharmacology has developed the Molecular Interaction Map (MIM) language, a tool that encodes biological information in a graphical form. The language allows simultaneous views of many interactions involving any given molecule, allowing MIMs to be used for analyzing bio-regulatory networks in the same way the circuit diagrams are useful for trouble-shooting electronic devices. Drs. Aladjem and Kohn have recently described electronic MIMs, which facilitate tracking of signaling pathways and allow easy access to annotations and data bases. The next step will be to elucidate the logic of signaling pathways from the multitude of molecular interactions depicted in the MIMs. Because of the complexity of information, this task is likely to be achieved by computer analyses. The proposed project will combine the MIM tools with Dr. McFadden's expertise in mathematical modeling to develop MIM-based computer simulations that will illustrate the processes by which cells govern DNA replication and cell cycle progression. AE.00.00.B5972
Stochastic Modeling of Contact Mechanics of Cantilevers for Calibrating Cochlear Models of Human Inner Ear (Joint NIST-NIH Program) Contact: Jeffrey T. Fong (Co-Adviser at NIH: Richard S. Chadwick) Complex cochlear models for improving understanding of the macromechanics, micromechanics, and nanomechanics of the transmission of sound in the inner ear can be validated experimentally using video microscopy to measure optical flow and atomic force microscopy (AFM) to measure intrinsic mechanical properties of the tectorial membrane. One of the key elements in the cochlear model validation process is the calibration of the stiffness (spring constant) of the force sensing cantilever with nano-tips. There are four classes of calibration methods currently in use: finite-element-analysis-based theoretical method, reference-sample-based force-deflection loading method, resonance-frequency-experiment-based geometric method, and the power-spectrum-experiment-based thermal method. The first three methods depend on the geometric and material properties of the nano-tip and thin film in contact. Deterministic and stochastic models of the contact mechanics of cantilevers with nano-tips, and the comparison of the simulation results of those models with uncertainty bounds are in progress to provide a rigorous basis for calibrating AFM, as well as for verifying the thermal method. AE.00.00.B6309 |
