Tuesday, October 20, 2015

CSjob: Postdoc, Computational Sensing Strategies for Low-Complexity Signal Models, Louvain, Belgium

Laurent Jacques let me know of this postdoc opportunity. From the page:
Applications are collected till December 15th 2015, last limit.

Open postdoctoral position:

ALTERSENSE: Computational Sensing Strategies

for Low-Complexity Signal Models
Discipline keywords: Compressive classification, Non-linear/1-bit/quantized compressive sensing, convex optimization, resource management, hyperspectral imaging, towards low-power sensor design
Location: ISPGroup, UCL, Belgium.


The research group of Prof. Laurent Jacques in the Image and Signal Processing Group (ISPGroup) of the University of Louvain-la-Neuve in Belgium (UCL) opens one position for a postdoctoral researcher to work on "ALTERSENSE" ("Computational Sensing Strategies for Low-Complexity Signal Models") a new project funded by the Belgian Fund for Scientific Research - FNRS.

AlterSense Project

With the steady development of technology in numerous scientific fields such as biomedical sciences, astronomy, optics or computer vision, big challenges are raised by the design of new and efficient data acquisition systems. These must often comply with contradictory goals such as sampling high dimensional domains, devising fast and low-complexity recording processes, reaching low-power consumption sensors, facing limited capacity communication channels and being at the same time robust against multiple noise sources.
Noticeably, the final objective of those sensors is invariably the same: providing at the very end of the data sensing chain processed and interpretable information, either for human or for automatic (machine) processing. This is the case, for instance,
  • in Satellite or Biomedical Imaging: for numerous imaging technologies, such as Hyperspectral Imaging, Magnetic Resonance Imaging, Computed Tomography, or Positron-Electron Tomography, segmenting images or data volumes in a few categories of connected pixel areas (e.g., spectral endmembers, biological tissues) is of particular interest for simplifying information;
  • in Low-Power Dynamic Sensing: in the general development of the Internet-of-Things (IoT) or of ultra-low energy sensors (e.g., biomedical).
As already formalized by Kolmogorov in the 60’s, all those applications are possible since “meaningful signals follow low-complexity descriptions”: their informative content is materialized by highly structured “patterns” whose intrinsic parameterization is considerably reduced compared to the high dimensionality of the ambient domain. By contrast, purely noisy signals often carry no information content, they are highly unstructured and require much more parameters to be characterized.
Leveraging the paradigm shift introduced by the Compressed Sensing theory where signal sensing is adjusted to prior signal models, ALTERSENSE aims to develop a “Computational Sensing” framework where, departing from the mere signal reconstruction objective, the sensing stage is adapted and simplified to perform specific computational tasks ahead of the final data processing. We will pursue this objective:
  • for ubiquitous data processing tasks: for detecting, segmenting or classifying informative signals;
  • for high-dimensional signals (e.g., hyperspectral or dynamic images) following low-complexity descriptions such as sparse/low-rank signal models or linear dynamical systems (LDS);
  • for conveniently balancing sensing time/complexity, data quantization and transmission (as in 1-bit CS), final data processing accuracy and data processing time as any other limited resources.
ALTERSENSE will also instantiate this theoretical research on two case studies with high scientific impacts, i.e., we will define computational sensing strategies for:
  • hyperspectral data volumes whose high dimensionality poses real challenges both in sensor design and data processing, with applications in biomedical and satellite imaging;
  • spatio-temporal event processing, in the general development of low-power (compressed sensing) sensors, e.g., for millimetric 2-D grid of EEG/ECG probes, efficiently detecting specific events (such as strokes).

Applicant's profile:

  • PhD in applied mathematics, electrical engineering or theoretical physics
  • Strong background in signal processing, compressed sensing and inverse problem solving.
  • Knowledge in measure concentration phenomenon, signal detection, classification methods, high dimensional data processing, convex optimisation.
  • Excellent programming skills in a numerical language (matlab or python);
  • Good communications skills, both written and oral, in English.
  • Knowing French *is not* required (the research group is international)
  • Mobility criterion: The position is open to all nationalities, including Belgian, but the condition is to have spent less than 12 months over the last three years in Belgium.
More information about the project can be obtained upon request.

We offer:

  • A research position in a dynamic environment, working on leading-edge theories and applications with international contacts;
  • A research team constituted of one professor, another postdoctoral researcher, and 3 PhD students on topics related to AlterSense;
  • A 24-month position funded by the Belgian NSF (FNRS)
  • The funding is a scholarship and Visa will be needed for a non-EU researcher.


Applications should include:
  1. a detailed resume (in pdf) + list of publications;
  2. 2-page research statement (in pdf) explaining also why the candidate is interested in working in the research topics described above and how it is connected to his/her PhD background
  3. the names and complete addresses of two reference persons that can be contacted
Please send applications by email to:

Candidate Selection

  • Pre-selection of candidates based on their application files
  • (remote) Interview of the short-listed candidates
  • The position remains open until selection of a good candidate
  • The successful candidate can be hired from January 1st 2016, but we have some flexibility to start the position (a bit) later (to be discussed)
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