Second doctoral position at RICAM in mathematical image processing
RICAM, the Johann Radon Institute for Computational and Applied Mathematics (Austrian Academy of Sciences), is looking for 1 doctoral student in applied and computational mathematics or computer science interested in pursuing research in computational harmonic analysis and partial differential equations for advanced image processing. We emphasize the interdisciplinary approach which will involve both a variety of mathematical tools and applications in art restoration. The student will be a member of the WWTF project "Five Senses - Call 2006", Mathematical Methods for Image Analysis and Processing in Visual Arts research project at RICAM, Linz, Austria.
Deadline for applications: September 15, 2007
Starting date of the PhD: November 1, 2007
The salary is determined according to the rules of the Austrian Research Fund FWF, i.e., 30.860,00 Euro/year brutto.
Work location: RICAM, Linz, Austria.
Applications should be addressed to Dr. Massimo Fornasier (firstname.lastname@example.org) and they should consist of:
- a presentation letter
- curriculum vitae et studiorum (including grades for each exam)
- diploma thesis, if already finished, title and abstract otherwise
- two letters of recommendation (to be addressed to the email above)
The ideal candidate has a Bachelor in Mathematics, Computer Science, or Information Engineering with courses successfully achieved in numerical analysis and experience in C/C++ and Matlab.
Further information about the project and the institute.
The time requirements for a PhD thesis is
typically in the range of 3 years. The doctoral studies will
be focussed (although not limited) on variational calculus
and inverse problems for image reconstruction and
applications in art restoration.
In particular, we are interested in investigating efficient algorithms for the minimization of functionals promoting sparse recovery. The algorithms may include randomized components. Studies on random matrices and subspace correction (domain decomposition) methods will be a crucial issue.
Depending on the particular interests of the doctoral student, related investigations in inverse problems for parameters detection in PDEs, compressed sensing, and learning theory can also be pursued.