CS: Compressive sensing by white random convolution, a job

Here is a new paper I just found: Compressive sensing by white random convolution by Yin Xiang, Lianlin Li, Fang Li. The abstract reads:

A different compressive sensing framework,convolution with white random waveform which has independent random entries subsampled at fixed (not random selected) locations is studied in this paper. We show that it wins high recovery probability for signals which are sparse in the representation basis which has small coherence, denoted by μ, with the Fourier basis. In particular, a n-dimensional signal which is S-sparse in such representation basis can be recovered with probability exceed 1-δ from any fixed m~O(μ2Slog(n/δ)3/2) samples gathered from the output of the random convolution, such as equal interval down-samples.

I also found a job announcement, it is in French. It can be found here. The INRIA announcement does not make a mention of compressed sensing unlike this entry (this is the same job) as the job doesn't require specifically a knowledge of CS.