Post-doctoral position in Grenoble, France.
A post-doctoral position is available at Gipsa-lab in Grenoble, France, to work with Simon Barthelmé and Ronald Phlypo on Graph Signal Processing and Gaussian Processes.
Graph Signal Processing (GSP) is a nascent field that aims to generalise tools used to process time series (line graphs) or images (regular 2D grids) to the setting of more general graphs. Examples include sensor networks, road networks, social networks, etc. One of the central ideas in GSP is to define an equivalent of the Fourier transform via the graph Laplacian, which opens up many ways to filter out noise, estimate signals, etc. From a statistical viewpoint, GSP techniques take graph structure as given, which is suboptimal. Indeed, in many cases graphs are imperfectly observed: missing or spurious links, missing nodes, and sometimes even entire parts of the graph that are missing. How then should we interprete the graph Laplacian and the methods that are constructed with it?
The goal of the post-doc is to explore the robustness and interpretability of GSP techniques under noisy structures. We will focus especially on the links between GSP and a special case of Gaussian processes called Gauss-Markov Processes.
We are looking for candidates with a background in statistics, signal processing, or machine learning. We will also consider candidates from statistical physics.
Description of the position
The position is a two-year post-doc funded by the ANR (French National Agency for Research, project GenGP). Salary is according to the French national scale (~ EUR 2,000 net per month).
Gipsa-lab is a research laboratory with a dynamic signal processing department, in which the candidate will be based. It is located in Grenoble, France. Grenoble is the largest city in the French Alps and features a very strong research environment in the physical and information sciences. It is also located at the heart of one of the most beautiful regions in France and offers many opportunities for those who love the outdoors, or think they might.
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(c) GdR 720 ISIS - CNRS - 2011-2018.