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SOCNET  June 2017

SOCNET June 2017

Subject:

PhD positions @ Ubiquitous Internet - IIT-CNR, Pisa

From:

Andrea Passarella <[log in to unmask]>

Reply-To:

Andrea Passarella <[log in to unmask]>

Date:

Wed, 14 Jun 2017 22:06:42 +0800

Content-Type:

text/plain

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Parts/Attachments

text/plain (265 lines)

*****  To join INSNA, visit http://www.insna.org  *****

Several PhD positions are open @ IIT-CNR, Pisa, Italy, on the following
topics
#1: Analysis of large-scale Online Social Networks (H2020 SoBigData)
#2: Distributed data analytics for IoT (H2020 SoBigData & AUTOWARE)
#3: Social-based Network Traffic Analysis for Cybersecurity (IIT
Cybersecurity Lab)

** Hosting University: IIT-CNR has multiple agreements for joint PhD
programmes
with the University of Pisa (http://phd.dii.unipi.it/en/,
https://www.di.unipi.it/it/phd) and
the University of Florence (http://smartcomputing.unifi.it/).

** Position type: doctoral fellowship, 3 years
** Starting date: fall 2017
** Location: IIT-CNR, Pisa, Italy - http://www.iit.cnr.it/
** Supervisor: Andrea Passarella - http://cnd.iit.cnr.it/andrea/
** Salary: EUR ~1200 per month (net)
** Application deadline: continuous evaluation, up until the end of July
2017

For all positions, it will be possible (and advised) to organise one
visiting
student period abroad (typically, 6 months) during the PhD.


Position #1: Analysis of large-scale Online Social Networks
-----------------------------------------------------------
Job description
---------------
The PhD activities will be focused on BigData analytics applied to data
crawled
from Online Social Networks. Specifically, the subject of the PhD will be on
(i)  collecting large-scale datasets from popular OSNs (e.g., Twitter),
and analyse
      the social network structures and the patterns of interactions between
      users through Big Data analytics techniques
(ii) designing new data-centric services which exploit knowledge about the
      extracted social network structures.

Successful candidates will be supervised by Dr. Andrea Passarella
(http://cnd.iit.cnr.it/andrea), and will work in the framework of the
H2020 SoBigData European Project, the EC-funded H2020 Research
Infrastructure
for social Big Data analysis (http://www.sobigdata.eu/).

The PhD activities will involve interdisciplinary approaches focusing on
a mix
of (i) efficient data crawling and collection techniques, (ii)
large-scale data
analysis, (iii) knowledge extraction, (iv) design of data-centric
services in
OSN platforms.


Candidate profile
-----------------
Ideal candidates should have or about to obtain a MSc degree in Computer
Science,
Computer Engineering, Physics, Statistics, or closely related
disciplines, and a
proven track record of excellent University grades.
Preferably, the topic of the MSc thesis should have been in one of the
relevant research areas (BigData analytics, OSN analysis/programming,
Complex
network analysis). Good written and spoken communication skills in
English are
required.




Position #2: Distributed data analytics for Internet of Things
-------------------------------------------------------------------------
Job description
---------------
The expected amount of data generated by pervasive devices in IoT
environments
calls for new distributed machine learning approaches, which depart from the
conventional model of collecting all data in huge data centres where machine
learning models are used to extract knowledge. Instead, data analytics is
performed on small datasets collected by individual nodes, which then
collaborate to learn more complex models. This approach is currently
explored,
among others by Google in the Federated Learning activity
(https://research.google.com/pubs/pub44822.html). It promises to be more
scalable, and to better preserve the users' privacy, with respect to
centralised
machine learning approaches.

One PhD position is open in this area. The PhD activities
will be focused on the design and evaluation of distributed data analytics
algorithms to be implemented on collaborating sets of networked nodes.
Distributed deep learning for Internet of Things environments will be a
specific
subject of investigation.

Successful candidates will be supervised by Dr. Andrea Passarella, and the
activities will be carried out in the H2020 FoF AUTOWARE European Project.

The PhD will work on a mix of these topics:
(i)   design and prototyping of distributed data analytics algorithms
for IoT;
(ii)  evaluation of the performance (e.g., with respect to centralised
solutions, in
       terms of accuracy and generated network traffic);
(iii) analysis of the performance bounds of the distributed analytics
algorithms


Candidate profile
-----------------
Ideal candidates should have or about to obtain a MSc in Computer Science,
Computer Engineering, Mathematics, or closely related disciplines,
and a proven track record of excellent University grades.
Preferably, the topic of the MSc thesis should be in one of the relevant
research areas
(IoT, mobile networking and computing, machine learning, BigData analytics).
Good written and spoken communication skills in English are required.





Position #3: Social-based Network Traffic Analysis for Cybersecurity
--------------------------------------------------------------------

Job description
---------------
Traditionally, network traffic monitoring tools have focused merely on
network-oriented metrics such as volume of data exchanged or top host
talkers. Recent cybersecurity attacks instead demonstrated that social
relationships have a great impact on network threats. These attacks
exploit social relationships such as a shared disk between friends or
people belonging to the same working group. To contrast cybersecurity
attacks of
this kind, novel analysis techniques need to be developed, which do not
focus
exclusively on packet-level analysis, but correlate traffic patterns
with the
properties of the nodes generating them (e.g., the same traffic pattern
might be
legitimate or not, depending on whether the communicating endpoints
belong to
the same user, to members of the same social community, or to complete
strangers).

The PhD activities will be focused on (i) learning how social relationships
influence network traffic data exchange (ii) designing new social-centric
algorithms and techniques that can be used to detect network traffic
anomalies
as well spot security infections and intrusions, with particular focus
on IoT
environments, where data must be analysed locally through decentralised
algorithms.

Successful candidates will be co-supervised by Dr. Andrea Passarella and
Dr. Luca Deri, and the activities will be carried out in the framework
of the
IIT-CNR Cybersecurity Lab.

The PhD activities will involve interdisciplinary approaches focusing on
a mix
of (i) network traffic analysis protocols and tools, (ii) large-scale
network
metrics analysis, (iii) mapping of social relationship with networks
activities,
(iv) behaviour-based network traffic modelling.

Candidate profile
-----------------
Ideal candidates should have or about to obtain a MSc in Computer Science,
Computer Engineering, or closely related disciplines, and a proven track
record
of excellent University grades.  Preferably, the topic of the MSc thesis
should
be in one of the relevant research areas (IoT, network traffic analysis,
network
measurement, mobile networking and computing, social networking). Good
written
and spoken communication skills in English are required.


=================================================



Research group
--------------
The PhD students will work in the Ubiquitous Internet group of IIT-CNR
in Pisa, Italy
(http://cnd.iit.cnr.it). UI activities range over multiple topics
related to the
design and analysis of Future Internet networking and computing systems,
including data-centric networks, mobile cloud, data analytics,
online/mobile social
networks, self-organising networks, hybrid wireless/wired networking and
computing. The UI group has a strong track record of successful
activities in
European projects, from FP6 to H2020, which is reflected in the many
international collaborations in EU and USA activated by the researchers
of the
group.


Application procedure
---------------------
Applications should consist of (all documents in English):
- a complete CV, including exams taken during the University degrees
(including
   the MSc final degree), with grades, and a link to the MSc. thesis
- a 1-page research statement showing motivation and understanding
   of the topic of the position
- at least one contact person (2 even better) who could act as reference(s)

The applications and any request of information should be sent to:
[log in to unmask], with subject, respectively:
"PhD application: Online Social Network Analysis",
"PhD application: Distributed data analytics for IoT", or
"PhD application: Social-based Network Traffic Analysis for Cybersecurity".

Applications will be continuously evaluated upon reception.
Applications will be considered until the position is filled, up until
the end of
July 2017. Multiple rounds of interviews will be organised with selected
candidates
while the positions are open. Interview will be scheduled based on the
received
applications, possibly also before the end of July 2017.

Selected candidates will have to apply for the formal public selections
to enter
one of the mentioned PhD programmes. Examinations typically take place
during
Fall (detailed will be provided to selected candidates as soon as
decided by the
Universities).


Contact point
-------------
For any additional information or clarification, please send a message to
[log in to unmask]


--
Andrea Passarella
-- 
Institute for Informatics and Telematics (IIT)
National Research Council (CNR)
Via G. Moruzzi, 1 voice: +39 050 315 3269
56124 Pisa, Italy fax: +39 050 315 2593
@/sip: [log in to unmask] mobile: +39 346 0082 540
 =======================================================================
Founding Associate EiC
Elsevier Journal on Online Social Networks and Media
http://www.journals.elsevier.com/online-social-networks-and-media/

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
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