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.
Introduction
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.
Application:
Applications should include:
- a detailed resume (in pdf) + list of publications;
- 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
- 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)
uclouvain.be
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