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The Applied Physics Laboratory at the University of Washington (APL-UW) is a national center for advanced science and engineering research and development and education. APL-UW was formed in 1943 for the U.S. Navy to bring university research resources to bear on urgent WWII defense problems. APL-UW has developed an international reputation for its broad based programs in science, engineering and for designing, building, and deploying the advanced technology required to meet the research needs of numerous government and commercial sponsors. With modern facilities, equipment and over 350 talented researchers and staff, APL-UW provides a unique, dynamic work environment with many opportunities.
APL has an outstanding opportunity for a Senior Research Scientist. This recruitment is open to UW Office of Research employees only.
The research conducted by the candidate will advance our knowledge of how to simulate and predict changes in the world’s glaciers and ice sheets. These areas have undergone unprecedented changes in the last several decades and is of particular interest from a U.S. perspective. A diversity of tools will be employed. These include in-situ observations, aircraft and satellite remote sensing data, and numerical models that are capable of synthesizing observations and numerical predictions to establish optimal estimates of glacial changes. The research will include development of community software tools that enable this approach.
The work consists of design and execution of numerical models of the flow of ice sheets to predict their future extent and volume. The inputs to such models are the current ice thickness and velocity, and material parameters such as the viscosity or the friction coefficient for ice sliding over the underlying bedrock. The thickness and velocity can be observed from satellites and planes, but the material properties cannot. The work will develop solutions how to estimate these unobservable material properties from the data we do have. From these inferences, we can gain vital information about the physics of glaciers. Example questions include whether a grounded glacier is frozen to the bedrock underneath, or does it flow over deformable sediments? This research incorporates ideas from continuum mechanics, thermal physics, glaciology, oceanography, and geology. The mathematical techniques used include Bayesian inverse problems and PDE-constrained optimization. The candidate will have expertise in these areas and the capability to develop software that integrates these concepts and provides numerical solutions. Familiarity with the numerical solutions of elliptic and hyperbolic partial differential equations (PDEs) is required. In addition, knowledge of object models for sparse matrices and graphs, preconditioners, iterative and direct solvers, and unstructured meshes is required.
The appointee will be independently responsible for study design (35% - develop technical strategy; evaluate project plans and criteria; assess feasibility of proposed methods and tests), execution of the analysis (20% - including project staffing decisions; management of resources, schedules, and work to optimize the analysis), the synthesis of data with numerical models (30%), and dissemination of results and data to the scientific community and the general public (15%). Study design involves the planning and acquisition of observational data, the design of numerical model experiments to advance our understanding of glaciers in the climate system. The appointee will disseminate the results in the form of peer reviewed publications, reports, and presentation at scientific meetings.
The appointee will have a demonstrated research record and high potential to develop their own well-funded program and has completed her/his postdoctoral research. This person will be developing as an authority with national recognition in glacier and ice-sheet dynamics.
Research Sponsors/Stakeholders (e.g. NIH, NSF, other schools or research institutions, etc.)
National Science Foundation
Doctoral degree in Applied Mathematics or other relevant field. Equivalent experience cannot be substituted for degree requirement.
At least six years of relevant work experience.
Proven experience in the development of glacialogical numerical models and data assimilation and the application of inverse modeling to infer unobserved variables.
Proven experience in scientific software development in C/C++, Python, and Fortran is required.
Demonstrated ability in writing competitive and successful proposals for grant-funded research and publication of postdoctoral research results.
Prior research experience involving areas such as continuum mechanics, thermal physics, glaciology, oceanography, and geology.
Familiarity with mathematical techniques including Bayesian inverse problems and partial differential equation (PDE)-constrained optimization; familiarity with the numerical solutions of elliptic and hyperbolic partial differential equations.
Demonstrated research record.
CONDITIONS OF EMPLOYMENT:
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