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Postdoctoral Fellow - Applied Mathematics Department

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POST DATE 9/2/2016
END DATE 10/29/2016

Lawrence Berkeley National Laboratory Berkeley, CA

Company
Lawrence Berkeley National Laboratory
Job Classification
Full Time
Company Ref #
81937
AJE Ref #
576076561
Location
Berkeley, CA
Experience
Mid-Career (2 - 15 years)
Job Type
Regular
Education
Doctoral Degree

JOB DESCRIPTION

APPLY
THE APPLIED MATHEMATICS DEPARTMENT IN THE COMPUTATIONAL RESEARCH DIVISION (CRD.LBL.GOV) IS LOOKING FOR TALENTED AND MOTIVATED POSTDOCTORAL FELLOWS TO BECOME PART OF OUR TEAM WORKING ON EXCITING RESEARCH PROJECTS IN APPLIED MATHEMATICS AND SCIENTIFIC COMPUTING. RESEARCHERS IN APPLIED MATHEMATICS AND SCIENTIFIC COMPUTING, OR ANY RELEVANT DISCIPLINE, WHO HAVE RECEIVED THEIR PH.D. WITHIN THE LAST THREE YEARS ARE ENCOURAGED TO APPLY. THE SUCCESSFUL APPLICANT WILL RECEIVE A COMPETITIVE SALARY AND EXCELLENT BENEFITS.

The Applied Mathematics Department develops advanced mathematical models and efficient computational algorithms for solving a broad range of scientific and engineering problems of interest to the Department of Energy (DOE), including in particular those related to energy and environment. Some of the current scientific and engineering areas include accelerator physics, astrophysics, climate, combustion, and seismic imaging. Many of the algorithms have scalable implementations that are targeted at current and next-generation massively parallel computer architectures, such as those available at the DOE National Energy Research Scientific Computing Center. Some of the implementations are also available in the form of the user-callable software frameworks and libraries. More details on the activities and projects in the Applied Mathematics Department can be found at http://crd.lbl.gov/departments/applied-mathematics/.

The successful applicant will be part of one of the research teams in the department working on a wide range of research and development projects and will join one of the three groups in the Applied Mathematics Department. These groups and their mission/focus, together with their current activities, are listed below.

Applied Numerical Algorithm Group (ANAG)

The Applied Numerical Algorithms Group (ANAG) develops advanced numerical algorithms and software for solving the partial differential equations which arise in problems of scientific and engineering interest. The primary focus of our work is in the development of high-resolution and adaptive finite volume/difference methods for partial differential equations in complex geometries with applications in a diverse range of fields such as compressible and incompressible fluid flows, plasma physics, porous-media flows, ice sheet modeling, carbon sequestration, climate, and astrophysics. The group's primary software product is Chombo, an open-source publicly distributed scalable adaptive mesh refinement (AMR) library that incorporates solver technologies developed in the group. These technologies include hyperbolic and elliptic solvers, PIC methods, and embedded boundary and mapped-multiblock approaches to complex geometries. Chombo is used regularly on platforms ranging from laptops to leadership-class supercomputer systems.

Ongoing efforts in the group fall into two broad categories. One is the development of novel algorithms, with a current focus on the design and implementation of higher order adaptive finite-volume methods more suitable for emerging architectures than existing algorithms. The second broad area involves working closely with the colleagues in CS research to incorporate and interface with developments in programming abstraction and other HPC matters. In these areas the current thrust is on DSLs, auto-tuning, asynchronous task scheduling and resiliency.

Center for Computational Sciences and Engineering (CCSE)

The Center for Computational Sciences and Engineering (CCSE) develops and applies advanced computational methodologies to solve large-scale scientific and engineering problems arising in DOE mission areas involving energy, environment, and industrial technology. The primary focus of CCSE researchers is on designing algorithms for multiscale, multiphysics problems described by nonlinear systems of partial differential equations, and in developing implementations of algorithms that target current and next-generation massively parallel computational architectures. CCSE researchers work collaboratively with application scientists to develop state-of-the-art solution methodologies in these fields.

In recent years, application areas have included combustion, porous media flow, mesoscale models for hydrodynamics, atmospheric modeling, cosmology and astrophysics. A common theme in a number of CCSE projects is low Mach number methods for weakly compressible flows, and the use of block-structured adaptive mesh refinement. Combustion efforts have focused on direct numerical simulation of laboratory-scale turbulent flames using detailed models for chemical kinetics. In the area of mesocale fluid mechanics we are developing stochastic continuum models that represent thermodynamic fluctuations in hydrodynamic variables and hybrid algorithms that combine continuum and particle descriptions of a fluid. We have also begun a new project area in which we are integrating simulation and experimental data to improve predictions of the behavior of complex systems.

Mathematics Group

The Mathematics Group develops new mathematical models, devises new algorithms, explores new applications, exports key technologies, and trains young scientists in support of DOE. We use mathematical tools from a variety of areas in mathematics, physics, statistics, and computer science, including statistical physics, differential geometry, asymptotics, graph theory, partial differential equations, discrete mathematics, and combinatorics. Rather than focus on a specific software or algorithmic approach, our orientation is to invent, implement, and use appropriate mathematics to tackle a range of scientific and engineering problems.

In recent years, application areas have included complex simulations of semiconductors, coating rollers, inkjet printing technologies and microfluid effects, foams in manufacturing processes, new metals, granular mixers, coal hoppers, rolling tires, mode-locked lasers, wind turbines, vibrating RF MEMS devices for wireless communications, and dynamic fracture in bulk metallic glasses. Another set of topics focuses on tools for the analysis of energy processes, and includes stochastic methods in environmental science, data analysis for meteorological data, data synthesis for wind energy and large-scale ocean currents, seismic imaging, image processing and analysis for analyzing cellular structures, and complex fluid-membrane solvers for understanding the dynamics behind cellular development in new biofuels and path planning for determining chemical accessibility in new materials such as zeolite and metal organic frameworks for gas separation sieves in carbon sequestration.

The successful candidate will have excellent oral and written communication skills, have a strong technical background in applied mathematics or scientific computing and be able to work effectively both in an independent fashion as well as part of a team.

Scalable Solvers Group (SSG)

The Scalable Solvers Group (SSG) develops and implements state-of-the-art algorithms for solving large-scale algebraic systems, including, but not limiting to, systems of linear equations, systems of nonlinear equations, and eigenvalue problems, on massively parallel computer architectures. Many of the algorithms are available and have been distributed in the form of software packages. By collaborating with domain scientists, the group applies the algebraic solvers developed to solve a variety of scientific problems that are central to the mission of DOE.

In recent years, the emphases have included scalable large sparse direct solvers, parallel multi-grid solvers, sparse eigenvalue solvers, randomized algorithms in numerical linear algebra, and fast sparse linear equations solvers based on data compression. Much of work is driven by the needs in the solution of large-scale scientific problems, which require close interaction and collaboration with domain scientists.



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