Project Specific Grants / Reserved topic scholarships 2017 - 1st call | Doctoral Program - Information Engineering and Computer Science

Project Specific Grants / Reserved topic scholarships 2017 - 1st call

FBK - Fondazione Bruno Kessler 

A1/A2 - Deep Learning for Machine Translation (2 grants)

Nowadays, human translation and machine translation are no longer antithetical opposites. Rather, the two worlds are getting closer and started to complement each other. On one side, the evolution of translation industry is witnessing a clear trend towards the adoption of Machine Translation (MT) as a primary support to professional translators. On the other side, the variety of data that can be collected from human feedback provides to MT research an unprecedented wealth of knowledge about the dynamics (practical and cognitive) of the translation process. The future is a symbiotic scenario where humans are assisted by reliable MT technology that, at the same time, continuously evolves by learning from translators activity. These grants aim to transform this vision into reality..
Contact: federico [at]

B1 - Strategic allocation of resources in highly distributed and heterogeneous computing and information systems under concurrency and multitenancy (1 grant)

The emerging paradigms for cloud computing involve multi-level systems where there exist different owners, multiple islands of the infrastructure and possibly edge components integrating additional hardware at the edge of the system.  Hence, the decision on how to allocate resources in such a dynamic and complex system involves the decision of several actors having possibly conflicting objectives, and the resulting operating point of the system is decided by their utilities, the economic figures into play and the mechanisms implementing pricing and resources matching. The candidate  will perform theoretical, algorithmic and simulation research on resource allocation problems in this context, using mathematical tools from game theory, optimal control and algorithmics.
Contact: fdepellegrini [at]

B2 - Security and privacy preservation in IoT discovery, authentication and access control through blockchain technologies (1 grant)

Blockchain can offer IoT devices a playground where they can be identified without the need of involving central trusted authorities (decentralised identity control) and the possibility to operate and interact within a trust-less environment. One of the big challenges of blockchain technologies is the lack of privacy mechanisms that avoid users to openly and publicly publish personal data on the blockchain public ledger.
Objective of this PhD is the study on novel processes, paradigms methods to preserve privacy when discovering IoT devices, when authenticating and offering access to them through blockchain technologies. Contact: fantonelli [at]

B3 - Embedded machine learning and AI techniques for cognitive IoT devices (1 grant)

Miniaturization and cost reduction of computing platforms, together with sensing and actuation electronics allow us to embed computation logic at a broader extent into real world objects, transforming them from dumb devices into smarter and more autonomous objects. Machine learning and artificial intelligence technologies, typically deployed on large scale computing infrastructure, have already proven their capability in the IoT domain to gain insights in the vast amount of data that IoT generates. The objective of the PhD is the study, analysis and experimentation on the optimal way to deploy and execute these kinds of algorithms on embedded devices, understand the related constraints of applicability and study approaches on how combine other distributed infrastructure (edge, cloud) supporting IoT applications in order to properly distribute the processing logic among embedded devices and the edge and cloud. 
Contact: fantonelli [at]

B4 - IoT for Smart Cities and Communities (1 grant)

The Internet of Things, including smart objects, wearables and wireless sensor networks, is becoming a key technology to enable Smart Cities and Communities. Key scenarios require connecting people and their environments through smart services supported by intelligent devices that observe and provide enriched contextual information. As such devices increasingly pervade physical spaces, challenges of sustainability arise in terms of scalability, device lifetime, and management of the massive amounts of generated data. We approach these challenges starting at the "smart thing" level, exploiting local processing to limit communication and adapting low power wireless communication protocols such as BLE and LoRa to offload information at low cost. Motivated by the challenges of Smart Cities and Communities, this research aims to address a range of challenges, to be balanced based on the curriculum of the candidate:
. define the "next smart thing" from an energy efficient embedded systems perspective, considering hardware architecture at system level and considering requirements arising from Smart Communities; 
. explore innovative machine learning approaches to be embedded on resource-constrained devices;
. face the energy efficiency challenge of IoT devices from the perspective of wireless communication, considering standard and non-standard communication stacks.
Contact: efarella [at] / murphy [at]

B5 - CMOS low power vision sensor for event detection (1 grant)

Videos are key elements for gathering evidence of an event occurring in the scene. In security and forensics, surveillance systems are widely employed to control indoor and outdoor environments, in order to prevent crimes. In this context, low-power imaging is an emerging technology, allowing vision systems to operate for months, powered with batteries, with no need for infrastructures.
The proposed research activity aims at developing novel CMOS vision sensor architectures integrating image sensing with efficient real-time processing on the same chip in order to detect anomalous events in the scene and taking low-level decisions. 
Contact: gottardi [at] 

C1 - Gamification for Smart Cities and Communities (1 grant)

In recent years, gamification has been successfully applied to increase people’s engagement, and to leverage contemporary ICT to promote participation and positive behavioral changes. FBK develops approaches and technologies for the gamification of dynamic and open-bounded socio-technical systems, such as Smart Communities and Smart Cities. Our research covers the whole game lifecycle, from game design to development, deployment, execution, monitoring and analytics. This topic offers several open research challenges:
. definition of concepts, models, and languages for the specification and design support of complex games that can apply to, and can be reused in, a variety of Smart City domains (e.g., sustainable mobility, energy conservation, participatory governance) and settings (e.g., various cities and communities);
. all techniques (e.g., machine learning, reccommender systems, planning) supporting the automatic generation of game logic and content that adapts the game experience of players to meet dynamic game objectives, match the personal player profile and ensure the engagement and retainment of players.

Contact: marconi [at] 

D4 - Satellite Image Time Series (SITS) analysis (1 grant)

Remote sensing sensors for Earth observation are experiencing a fast-technological development. Images with enhanced features are available showing better trade-off in terms of spectral, spatial, and temporal resolution. Information extraction and retrieval from such data requires the design, implementation and validation of novel methodologies and algorithms based on pattern recognition, image/signal processing, machine learning and/or data fusion. 
In the above context, the Remote Sensing for Digital Earth Unit at Fondazione Bruno Kessler is looking for a Ph.D. student candidate to work on long image time series.
Besides the general requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:
. master degree in fields like Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;

. knowledge in pattern recognition, image/signal processing, statistic and/or remote sensing.
Contact: bovolo [at] 

DISI -Department of Information Engineering and Computer Science

D1 - Methods and techniques for the processing of signals acquired by the satellite radar sounder RIME (Radar for Icy Moon Exploration) for the exploration of the Jupiter icy Moons (1 grant) 

The research activity that will be developed is related to the Radar for Icy Moon Exploration (RIME) in the framework of the JUpiter ICy moon Explorer (JUICE) mission of the European Space Agency. RIME is developed under the leadership of the University of Trento in cooperation with Italian Industry and the Jet Propulsion Laboratory (JPL) in US with the funding of Italian Space Agency and NASA, respectively. RIME has an international Science Team that includes scientists from many different institutions in Europe and US. 

RIME is a radar sounder (ground penetrating radar from satellite platform) defined to study the geology, the geophysics and the possible presence of water in the subsurface (up to 9 km) of the Jupiter icy moons, i.e., Ganymede, Europa and Callisto.

The research activities related to the PhD position can address different specific directions (to be agreed with the selected candidate) including the definition, design, implementation and validation of: 1) data analysis techniques for supporting the automatic generation of products for the RIME ground segment; 2) radar signal processing algorithms; 3) radar acquisition strategies for addressing the geophysics and geological challenges of the study of the Jupiter Icy Moons (Europa, Ganymede, and Callisto); and 4) techniques for the joint analysis of RIME data and data acquired by other JUICE instruments (e.g., the laser altimeter and the camera). The PhD student will work at the Remote Sensing Laboratory of the University of Trento. 

For more information on the activity of RSLab refer to: or contact Prof. Lorenzo Bruzzone lorenzo.bruzzone [at]

D2 - Methods and techniques for the processing of signals acquired by the satellite radar sounders for Earth observation (1 grant) 

The research activity that will be developed is related to the definition and the design of a new Earth Observation mission based on a radar sounder for the analysis of the Earth subsurface. The main goals of the mission are related to the study of the sub-surface of the polar ice sheets and of the subsurface of arid areas. This has a huge impact on the study of the cryosphere and the climate, as well as on the detection and analysis of the water table in the desert. 

The research activities related to the PhD position can address different specific directions (to be agreed with the selected candidate) related to the main problems to be solved for this kind of mission, including the definition, design, implementation and validation of: 1) data simulation techniques for radar performance assessment; 2) strategies and techniques for mitigating the effects of the ionosphere on the radar signal; 3) radar signal processing techniques; 4) techniques for clutter reduction; and 5) data analysis techniques for the automatic extraction of information from radargrams for the generation of mission products. The PhD student will work at the Remote Sensing Laboratory of the University of Trento. 

For more information on the activity of RSLab refer to: or contact Prof. Lorenzo Bruzzone lorenzo.bruzzone [at] (l)lorenzo.bruzzone [at]

D3 - Methods and techniques for automatic analysis and fusion of multisource remote sensing images and data (1 grant)

The research activity is focused in the field of satellite remote sensing systems and is devoted to the development of advanced automatic methods for the analysis and the fusion of multisource remote sensing images and data. The goal is to define, design, implement and validate methods based on pattern recognition and machine learning for the analysis of remote sensing images acquired by different sensors and for the integration/fusion of these images with other ancillary data. Different paradigms will be studied related to the most recent methodological developments in the framework of information extraction and fusion. Specific attention will be devoted to the integration of remote sensing data with the data derived through physical-based models. 

The general methods developed will be then customized on the problems of cryosphere monitoring and hydrological parameter estimation. In this framework, an extensive validation of the developed methods will be carried out by using different kinds of remote sensing data, physical-based models and ancillary data. The PhD student will work at the Remote Sensing Laboratory of the University of Trento. 

For more information on the activity of RSLab refer to: or contact Prof. Lorenzo Bruzzone lorenzo.bruzzone [at]


A3- Computational methods for brain connectivity analysis (1 grant) [additional reserved topic scholarship]

The PhD aims at carrying out research activity on machine learning methodologies for brain connectivity data analysis. The main goal is to design and to deploy machine learning algorithms for open challenges such as the detection of the main structural and functional pathways of the brain, the characterisation of the differences with respect to altered brain connections, the inter-individuals analysis of brain connectivity structures.
The PhD grant is jointly supported by Fondazione Bruno Kessler (FBK) and Istituto Italiano di Tecnologia (IIT). The research activity will take place at the Neuroinformatics Laboratory (NILab) and Pattern Analysis and Computer Vision Laboratory (PAVIS)
avesani [at] - diego.sona [at]