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PhD positions available at IIT-CNR, Pisa, Italy
martes, 23 de junio de 2020

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
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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
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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:
Esta dirección de correo electrónico está protegida contra los robots de spam, necesita tener Javascript activado para poder verla , 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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