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 PhD student to work on "ALTERSENSE" ("Computational Sensing Strategies for Low-Complexity Signal Models") a new project funded by the Belgian Fund for Scientific Research - FNRS.
Description
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 Imaging: in the general development of the Internet-of-Things (IoT), e.g., for networks of compact and ultra-low-power imagers interacting with each other, efficiently detecting specific events in an environment (e.g., object motion) without fully sampling scene images can simplify sensor design.
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., hyperspectral imaging and dynamic video imaging.
Nr of positions available : 1
Research Fields
Mathematics - Applied mathematics
Engineering - Electrical engineering
Career Stage
Early stage researcher or 0-4 yrs (Post graduate)
Research Profiles
First Stage Researcher (R1)
Comment/web site for additional job details
sites.uclouvain.be/ispgroup/index.php/Positions/PhDAlterSense
Requirements
| ENGLISH |
| Excellent |
| Mathematics |
| Engineering |
| * Skilled and motivated student with high grade point average in bachelor and master programs. * M.Sc. in Applied Mathematics or Electrical Engineering * The student must have a good understanding of signal processing, optimisation methods, information theory. * The student must be able to program efficient methods in either Matlab or Python. * Assets are: knowledge of compressive sensing theory and applications, sparse data representations, sparse coding, hyperspectral imaging and video processing. * Excellent communications skills, both written and oral, in English. |
| Master Degree or equivalent |
| Master Degree or equivalent |