topics:
#1: Big Data Analysis and Decision Support Systems for Smart Healthcare
applications
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** Position type: Research Assistant, 12 months
** Scientific Supervisor: Franca Delmastro -
https://www.iit.cnr.it/franca.delmastro/
https://scholar.google.it/citations?user=fiw73vIAAAAJ
** Net salary: ~ EUR 1500 per month
==========================================================
#2: Cooperative Ubiquitous Opportunistic Charging with Intelligent
Battery Aging Mitigation
==========================================================
** Position type: Postdoctoral Fellowship, 12 months
** Scientific Supervisor: Theofanis Raptis -
https://www.iit.cnr.it/theofanis.raptis/
https://scholar.google.com/citations?user=aDoDo_kAAAAJ
** Net salary: ~ EUR 1625 per month
==========================================================
** Starting date: Q1 2021
** Location: Ubiquitous Internet Research Unit (RU) @ IIT-CNR, Pisa,
Italy - http://www.iit.cnr.it/
** RU Leader: Andrea Passarella -
https://scholar.google.it/citations?user=sesKnygAAAAJ
** Application deadline: continuous evaluation, up until mid-February
2021
Position #1: Big Data Analysis and Decision Support Systems for Smart
Healthcare applications
==========================================================
Job description
---------------
Smart Healthcare applications represent today a hot research field,
including remote health monitoring solutions enriched with AI algorithms
for the automatic detection and prediction of risky and adverse health
conditions of specific user categories (e.g., frail older adults,
subjects affected by chronic diseases). To this aim, data derived from
IoT systems are enriched with sensing data derived from personal and
wearable devices in order to provide a complete picture of the user's
health status, including physical, cognitive and social conditions, on a
long-term period.
A research assistant position is open in this area for the definition
and evaluation of AI algorithms for anomalous behaviour detection and
risk prediction both in specific health domains (mainly nutrition, sleep
disorders, and stress conditions) and integrated in a multidimensional
evaluation of frailty condition in older adults. These algorithms should
also be designed to be integrated in a mobile health (m-health) solution
to guarantee the processing of sensitive data directly on the personal
mobile device and to provide a personalised and motivational feedback to
the monitored subject. The algorithms will be validated both on
available datasets and on new datasets collected through the application
of prototype solutions in real scenarios and pilot studies based on the
collaboration with medical units.
The Research assistant will work on the following topics:
(i) Design and evaluation of AI algorithms for anomalous behaviour
detection and risk prediction based on heterogeneous sensing data.
(ii) Integration of the proposed algorithms in a prototype m-health
application.
(iii) Participation in the definition and deployment of pilot studies
with real subjects.
Candidate profile
---------------
Candidates should have a MSc degree 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, e-health
applications).
Good written and spoken communication skills in English are required.
The selected candidate could also be interested in applying to a PhD
program in Fall 2021.
Specifically, 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/)
The selection for PhD programs is managed by the related hosting
university and applicants will apply directly to the official call of
the specific PhD.
==========================================================
Position #2: Cooperative Ubiquitous Opportunistic Charging with
Intelligent Battery Aging Mitigation
==========================================================
Job description
---------------
Battery aging (the loss of battery's capacity and internal resistance
growth) is an emerging challenge for portable devices carried by mobile
users. Due to the fact that newer devices are becoming more feature-rich
and resource demanding, they are subjected to more charge-discharge
cycles compared to their older counterparts; a pattern which increases
their battery aging, and enables them to require higher power and more
expensive and bigger batteries. Individualized battery aging mitigation
and user profiling, which do not take into account neither other users
in the networked population nor emerging energy sharing technologies
like peer-to-peer wireless power transfer, are not uncovering the full
potential of intelligent charging.
One post-doc position is open in this area, for the design and
deployment of ubiquitous charging intelligence in order to cooperatively
mitigate battery aging, radically optimize energy redistributions, and
unorthodoxically increase user quality of experience. The envisioned
approach will deliver fine-grained modelling and algorithmic
exploitation of not only static wired charging but also wireless energy
sharing technologies, and will provide a novel combination of
theoretical tools, socio-technical extensions, practical simulations and
(potentially) limited field trials.
The Postdoctoral Fellow will work on the following topics:
(i) Rigorous socio-technical modelling for populations of cooperative
mobile users
(ii) Algorithmic development for intelligent, network-wide,
wired/wireless charging with battery aging mitigation
(iii) Performance evaluation through large scale simulations and/or real
device experimentation
Candidate profile
---------------
Ideal candidates should have (or are about to obtain) a PhD in Computer
Science, Computer Engineering, or closely related disciplines, and a
proven track record of publications in relevant top-tier journals and
conferences. Preferably, the topic of the PhD should have been in one of
the related research areas (mobile computing, WSNs, opportunistic
networks, IoT). Good written and spoken communication skills in English
are required.
==========================================================
Research Unit (RU)
---------------
The positions will be in the Ubiquitous Internet (UI) RU of IIT-CNR in
Pisa, Italy (http://cnd.iit.cnr.it). UI activities range over multiple
topics related to the design and analysis of the Next Generation
Internet networking and computing systems, including edge computing,
Internet of People, decentralised AI, human-centric networked systems,
online/mobile social networks, data centric and self-organising
networks. Verticals of interest include Industry 4.0, e-health, energy
efficiency, smart mobility. The RU has a strong track record of
successful activities in national and European projects, from FP6 to
H2020, which is reflected in the many international collaborations in EU
and USA activated by the researchers of the RU.
Application procedure
---------------
Applications should consist of (all documents in English):
- a complete CV, with a complete transcript of the university grades,
both at the MSc and PhD levels
- a 1-page research statement showing motivation, understanding and
knowledge on the topic of the position
- contact details of 1-2 persons who could act as reference(s)
The applications and any request of information should be sent to:
** Position #1: franca.delmastro@iit.cnr.it with subject "Research
assistant application: smart healthcare"
** Position #2: theofanis.raptis@iit.cnr.it with subject "Post-doc
application: cuochi.bam"
Applications will be continuously evaluated upon reception. Interviews
will be organized with selected candidates. Applications will be
considered until suitable candidates are identified up to mid-February
2021. Interested candidates are thus strongly encouraged to send their
application as soon as possible.
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