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The Numerical Analysis and Applied Mathematics (NUMA) research unit of the Department of Computer Science at the KU Leuven - University of Leuven (Belgium) invites applications for up to 6 doctoral and 3 postdoctoral positions.

NUMA consists of 38 members (professors, postdoctoral and PhD researchers). We develop, analyse and implement numerical algorithms for applications in science and engineering. We focus on algorithmic innovations, and on the analysis of accuracy and efficiency. Our research relies on mathematical insight, design of algorithms, and software development.

NUMA has vacancies on several recently acquired research projects for researchers with various expertises. Applicants should have a master’s degree in (Applied) Mathematics, Computer Science, Engineering or Physics, should have experience in programming and a general interest in the development and analysis of numerical methods, and/or their application in engineering and science. We are currently looking for candidates with specific interest/expertise in one (or more) of the following domains:

  • Numerical (multi)-linear algebra: polynomial computations and iterative methods
  • Multiscale and stochastic simulation methods
  • Large-scale PDE-constrained optimization and uncertainty quantification
  • Parallel algorithms and metaheuristics for cutting and packing problems
  • High-dimensional approximation, simulation and analysis
  • Computational methods for control
  • Data driven network science

We offer a stimulating environment at a European top university, a well-equipped and experienced multidisciplinary research unit, a personalized PhD trajectory, with attention paid to transferable skills development and a competitive wage.Senior postdocs will get the opportunity to become involved in project management.

For more information on the research unit and possible supervisors, visit https://wms.cs.kuleuven.be/groups/natw/

The positions at the postdoctoral level are for one year with the possibility of extension to a second year. The positions at the predoctoral level will be initially for one year. After a positive evaluation, this position can be extended for three more years. Candidates are invited to submit their application (including a detailed CV, a motivation letter, and two letters of recommendation) via the websites https://icts.kuleuven.be/apps/jobsite/vacatures/54213421 (for the PhD positions) and https://icts.kuleuven.be/apps/jobsite/vacatures/54218508 (for the postdoc positions).

We are looking for a full time postdoctoral researcher in the Computer Science Department at Purdue to work on a new class of algorithms in numerical linear algebra that are robust to faults and failures in distributed systems and the next generation of exascale machines.

An ideal candidate will have expertise and experience in:

  • matrix computations and sparse matrix computations, with some background on the convergence theory of stationary algorithms or Krylov methods
  • distributed and large scale implementations of matrix computations in engineering applications, such as linear system solvers, eigenvalue computations, and advanced matrix-based data analytics (e.g. PCA, clustering, etc.)

We will consider exceptional candidates with experience in only one of these areas who wish to develop expertise in the other area. The postdoctoral advisors are David Gleich & Ananth Grama.

Please send application materials to matrix-postdoc-2017@cs.purdue.edu

Materials should consist of:

  • A 1-2 paragraph introduction explaining why you want the position.
  • A standard CV with your publications highlighted.
  • Two to four people who could provide references.
  • (Optional, but encouraged) an annotated CV explaining your specific contribution to 1-3 publications on your CV in 1-2 paragraphs each. Highlight the intellectual depth and engineering efforts in your work.

Applications will be screened immediately upon receipt and will be accepted until the position has been filled. The starting date for the postdoc is negotiable, but no later than Fall 2017. This information can be easily shared through the web-page https://www.cs.purdue.edu/homes/dgleich/matrix-postdoc-2017.html