PhD positions are open @ IIT-CNR, Pisa, Italy, on the following topics
#1: Human-centric Artificial Intelligence (H2020 HumanE-AI-Net &
SoBigData++)
#2: Analysis of large-scale Online Social Networks (H2020 HumanE-AI-Net &
SoBigData++)
#3: Serverless computing in the device-to-cloud continuum (H2020 MARVEL)
** Hosting Universities:
IIT-CNR is part of several PhD programs including
- the PhD program in Data Science (https://datasciencephd.eu/)
hosted by the Scuola Normale Superiore (https://www.sns.it/en)
- the PhD programs in Computer Engineering (https://phd.dii.unipi.it/en/)
and Computer Science (https://dottorato.di.unipi.it/),
hosted by the University of Pisa
- the PhD program in Smart Computing, jointly organised by
the Universities of Florence, Pisa, Siena, CNR, FBK
https://smartcomputing.unifi.it/
Selected applicants shall apply to the official call of the specific PhD
PhD positions
-------------
** Position type: doctoral fellowship, 3 years
** Starting date: fall 2020
** Location: IIT-CNR, Pisa, Italy - http://www.iit.cnr.it/
** Supervisors:
Marco Conti, Andrea Passarella
https://scholar.google.com/citations?user=KniFTD0AAAAJ
https://scholar.google.com/citations?user=sesKnygAAAAJ
** Annual scholarship: EUR 15000 - 17000 (depending on the program)
** Application deadline: continuous evaluation, up until 1st July
****************************************************************************
******
*
* Interviews with selected candidates will be organised between now and
* 1st of July based on received applications. The posts will be filled
* as soon as suitable candidates are identified.
* Interested candidates are thus strongly encouraged to send their
application
* as soon as possible.
*
For all positions, it will be possible (and advised) to organise one
visiting student period abroad (typically, 6 months) during the PhD.
Position #1: Human-centric Artificial Intelligence
-----------------------------------------------------------
Job description
---------------
AI systems are increasingly moving from a centralised, black-box approach to
more decentralised approaches where "smaller" AI systems operate closer to
the final users, possibly also on their own devices, and interact with each
other.
In addition, an exciting challenge is how to make AI systems automatically
adapt to seamlessly interact with the users, forming a hybrid
human-artificial ecosystem where both actors (the human and the AI system)
work together in a collaborative way.
Both challenges fall under the umbrella of "human-centric AI", as the
emphasis is on designing AI systems that inherently embed models of the
humans individual and collective behaviour, and interact directly with human
users.
For example, the PhD thesis could be on decentralised forms of AI, where
multiple "local" AI components interact with each other, combine local
knowledge to come up with collective AI models. Human behaviour models will
be used to drive the design and operations of both local and collective AI
systems.
Successful candidates will be supervised by Dr. Marco Conti and Dr. Andrea
Passarella.
The PhD activities will involve a mix of modelling, systems/algorithms
design, prototype development, performance evaluation via experiments,
analysis, simulation.
Candidate profile
-----------------
Candidates should have or about to obtain a MSc degree (at the latest by
31st October 2020) 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 (Artificial Intelligence, BigData analytics, distributed
systems).
Good written and spoken communication skills in English are required.
Position #2: Serverless computing in the device-to-cloud continuum
------------------------------------------------------------------------
Job description
---------------
Serverless computing is emerging as a dominant model in the cloud market.
It allows to execute code on virtualised infrastructure with the promise of
automated infinite scalability, coupled with fine-granularity pay-per-use
billing. This has led to the growing popularity of the
Function-as-a-Service paradigm, where the developers write microservices as
independent "functions", which are then packaged together to compose the
overall application. These functions are stateless, which limits the
applicability of FaaS and encourages adoption of (possibly inefficient)
work-around solutions to execute stateful services. How to deploy and
optimise and run-time stateful serverless applications is an open research
area.
Furthermore, the advantages of serverless are so far confined to data
centres, which have homogeneous compute resources highly inter-connected.
However, it is generally agreed that the balance of computation is shifting
towards the edge of the network to achieve lower latencies and higher
throughput, hence enable future applications (virtual/augmented reality,
automated guided vehicles, haptic feedback interfaces, ...). We can even
envisage that end user devices, which are thus beyond the edge of the
network, will host the execution of some microservices to fully exploit the
opportunities offered by this paradigm.
The PhD activities will be focused on the design and evaluation of protocols
and algorithms to implement efficiently serverless, both stateless and
stateful, in the continuum from end user devices to the cloud.
Successful candidates will be supervised by Dr. Marco Conti and Dr.
Andrea Passarella.
The PhD will work on a mix of these topics:
(i) modelling of distributed computing systems, taking into
account the specific characteristics of serverless systems
with heterogeneous infrastructures (device, edge, cloud)
(ii) design of decentralised algorithms for the efficient
orchestration of lightweight OS virtualisations (e.g.,
containers) hosting the application code and, if needed, state
(iii) performance evaluation of the proposed solutions, compared
to state-of-the-art alternatives both in the market and in
the scientific literature; this may involve the development
of a prototype and its integration with existing serverless
frameworks and simulation/emulation tools
Candidate profile
-----------------
Candidates should have or about to obtain a MSc degree (at the latest by
31st October 2020) in Computer Science, Computer Engineering, or closely
related disciplines, and a proven track record of excellent University
grades.
Preferably, the topic of the MSc/PhD thesis should be in one of the relevant
research areas (cloud or edge technologies, distributed or opportunistic
computing, mobile networking and computing). Good written and spoken
communication skills in English are required
Position #3: Analysis of large-scale Online Social Networks
Job description
---------------
Online Social Networks are one of the main sources of Big Data to analyse
the human social behaviour, and design smart human-centric services that
exploit this knowledge. BigData collection and analysis allows to use OSN as
a "big data microscope" to characterise human behaviour and design novel OSN
systems accordingly. The activities of the PhD will be focused on BigData
analytics applied to data crawled from Online Social Networks.
Specifically, the subject of the PhD thesis 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, with applications
to the analysis of complex socio-technical phenomena such as
migrations,
information and fake news diffusion, opinion polarisation, social and
mental well-being
(ii) designing new data-centric services which exploit knowledge about the
extracted social network structures.
Successful candidates will be supervised by Dr. Marco Conti and Dr. Andrea
Passarella.
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
-----------------
Candidates should have or about to obtain a MSc degree (at the latest by
31st October 2020) 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 (BigData analytics, OSN analysis/programming, Complex network
analysis).
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 decentralised AI, data-centric networks, edge
computing, online/mobile social networks, self-organising networks. 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. All
three positions are open on recently started or about to start
H2020 projects (specifically, SoBigData++, HumanE-AI-Net and MARVEL).
Application procedure
---------------------
Applications should consist of (all documents in English):
- a complete CV, including exams taken during the University degrees with
grades,
including, if already completed, the MSc final degree), 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:
Este enderezo de correo está a ser protexido dos robots de correo lixo. Precisa activar o JavaScript para velo., with subject, respectively:
"PhD application: Human-centric Artificial Intelligence"
"PhD application: Serverless computing in the device-to-cloud continuum"
"PhD application: Analysis of large-scale Online Social Networks"
Contact point
-------------
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