 |
 |
Job Title: Research Associate in Mathematics & Computing, Imperial College London Level: Academic and Research, Level B Fixed term: Commencing 1 October 2011 or soon thereafter, for 24 months This position is funded by the Engineering and Physical Sciences Research Council (EPSRC) as part of a large joint project on Enabling High-performance Statistical Computing in R on Hybrid GPU and Multicore Architectures. The project involves collaboration between Dr G. Montana and Prof. Y. Guo (Imperial College London) and Prof J. Dongarra (Manchester University). The R system for statistical computing is a popular, open-source platform used world-wide by a very large community of statisticians, engineers, physicists and other scientists. R is used across a diverse range of applications areas including bioinformatics/genomics, cosmology, particle physics, astronomy and image processing. A key reason for R's popularity is its high-level, easy to learn language for "programming with data", that enables users to perform many common data analytic tasks, including organization and manipulation of data sets, fitting statistical models, producing graphics, and executing a vast range of numerical computations and simulations. This project aims to enhance the capabilities of the R system for statistical computing by developing a framework for high-performance computing that takes full advantage of the specialized processing capabilities offered by the latest generation of 'commodity' hardware. We recognize that almost all of today's statistical and analytical approaches rely heavily on the performing of common mathematical computations such as matrix algebra operations, and we seek to contribute to the development of a new generation of extremely fast mathematical libraries that will utilize the special processing capabilities of the modern multicore and graphical processing units (GPU), that are now found in all personal computers and laptops. The Research Associate will join an existing team of developers to work on a new generation of linear algebra libraries that achieve the fastest possible time to an accurate solution on hybrid multicore and GPU-based systems, and will design and develop high-level interfaces to these linear algebra libraries by embedding them into the R system for statistical computing. It is essential that the successful candidate have: PhD or equivalent qualification in Mathematics, Statistics, Computer Science, or a related subject area, or have equivalent research experience. Experience of research in numerical analysis and preferably in numerical linear algebra. Demonstrated ability to carry out research and development of algorithms and software in numerical analysis. Experience of software development in C, preferably with a working knowledge of numerical libraries such as LAPACK, ScaLAPACK, PLASMA, MAGMA or similar. • Experience of developing codes for parallel and heterogeneous computers, preferably using MPI, Open-MP or CUDA. Broad interests in scientific computing and be able to communicate with a wide range of researchers in mathematics, computer science and application areas as may be required to pursue an interdisciplinary research project. Excellent communication skills, with a demonstrated ability to effectively communicate research results at international level both in writing and in spoken form at conferences. Be able to work independently but also within the project team. For further informal enquiries please contact Dr Giovanni Montana, email: [log in to unmask] Should you have any queries regarding the application process, please contact Mrs Rusudan Svanidze, Tel: +44 (0)207 594 8555; Email: [log in to unmask] You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.