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-------- Forwarded Message --------
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">Subject:
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<td>Ph.D. positions at University of Catania, Italy</td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">Date: </th>
<td>Thu, 07 Jul 2016 09:56:15 +0200</td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">From: </th>
<td>Image Processing Laboratory <a class="moz-txt-link-rfc2396E" href="mailto:iplab@dmi.unict.it"><iplab@dmi.unict.it></a></td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">To: </th>
<td><a class="moz-txt-link-abbreviated" href="mailto:wp@cg.tuwien.ac.at">wp@cg.tuwien.ac.at</a></td>
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<br>
<br>
**** We apologise for cross-postings ****<br>
**** Please forward this e-mail to potentially interested
researchers ****<br>
<br>
We are seeking for two exceptional and passionate full-time Ph.D.
students to
work on two R&D projects jointly supervised by the University
of Catania
(Sicily, IT) and two involved partners (see all the details
below).<br>
<br>
The Image Processing Laboratory (IPLab) is part of the department
of Mathematics
and Computer Science of the University of Catania, Italy. IPLABs
research
focuses in the areas of Image Processing, Computer Vision, Machine
Learning and
Computer Graphics. IPLAB has been involved in different
international projects
for the development of advanced algorithms with applications in
different
domains: Embedded and wearable devices, assistive technologies,
quality of life,
forensics, medical, cultural heritage.<br>
<br>
The student will have the possibility to work in a friendly and
constructive
environment and will have the possibility to attend at the major
conferences and
schools in the field to present the results of his/her research.<br>
<br>
Equal opportunities to all applicants will be provided. Any
expertise or prior
knowledge in computer vision, machine learning and image
processing is welcome.
Prior publication at international conferences is an advantage.
Ability to
program in Matlab/python/C/C++ is desirable. Other programming
languages,
communication skills and team play will also be welcome.<br>
<br>
If you know any good candidates, please feel free to forward this
email. Ask
them to send CV, a cover letter, the publication list (if any) to
Prof Sebastiano Battiato (<a class="moz-txt-link-abbreviated" href="mailto:battiato@dmi.unict.it">battiato@dmi.unict.it</a>) and Dr. Giovanni
Maria Farinella (<a class="moz-txt-link-abbreviated" href="mailto:gfarinella@dmi.unict.it">gfarinella@dmi.unict.it</a>)<br>
<br>
Application procedure will be formally opened in the forthcoming
weeks (mid of
July 2016).<br>
<br>
************<br>
PhD Position A<br>
************<br>
<br>
* Ambient Assisted Living with focus on Fall Detection *<br>
<br>
* University of Catania IPLAB (Catania, Italy) & OSRAM
(Munich,DE) *<br>
<br>
* Academic Supervisor: Dr. Giovanni Maria Farinella:
<a class="moz-txt-link-abbreviated" href="mailto:gfarinella@dmi.unict.it">gfarinella@dmi.unict.it</a><br>
<br>
* Brief Description *<br>
The main aim of this PhD. programme is the design and development
of a low-cost
and noninvasive Fall Detection System based on visual data
acquired with
fish-eye cameras mounted on the roof of a room (e.g., in a
roof-lamp). Attention
will be taken in the development of a solution able to work in
embedded systems
with weak supervision for training (offline) and/or automatic
training after the
installation (online). The Fall Detection solution will be
designed and
developed by keeping in mind indoor environments such as Homes,
Hospital Rooms
and Nursing Homes & Clinics.<br>
<br>
************<br>
PhD Position B<br>
************<br>
<br>
* Learning Architectures for Multimedia Representation and
Understanding<br>
<br>
*University of Catania IPLAB (Catania, Italy) &
STMICROELECTRONICS (Catania,
Italy)<br>
<br>
* Academic supervisor: Prof Sebastiano Battiato:
<a class="moz-txt-link-abbreviated" href="mailto:battiato@dmi.unict.it">battiato@dmi.unict.it</a><br>
<br>
* Brief Description *<br>
The aim of this Ph.D. programme is to use data coming from
different domains
(images, video/temporal/motion, GPS, audio signals, tags, wearable
sensors,
etc.) to build a powerful representation, possibly extending the
current CNN to
treat the different signal jointly and in a semi-supervised way
(i.e., with no
need of huge datasets to perform training for personalized use).
The application
contexts will consider emerging scenarios, such as activity
monitoring and
assistive technologies to improving health and wellness of human
beings and
quality of life in general.<br>
<br>
<br>
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