Page related to first call year 2016 available  at

FBK - Fondazione Bruno Kessler 

A - Quantifying Historical Cultural Evolution using Big Data (1 grant)

The Phd candidate will exploit text processing techniques to discover temporal and spatial relations in large semi-structured data (e.g. Wikipedia). These relations will be used to build a storyline capturing the interactions between prominent people (writers, artists, scientists, politicians, etc), their cultural influence and their legacy in the evolution of the culture. The project will involve two research groups in FBK, Digital Humanities (DH) and Mobile and Social Computing Lab (MobS) and will be conducted also in collaboration with the MIT MacroConnections group at MIT Media Lab, lead by Prof. Cesar Hidalgo.

DISI -Department of Information Engineering and Computer Science

B - Network and Application Protocols for Cooperative Driving and Beyond (1 grant) 

Cooperative driving is the new frontier beyond self driving cars. Vehicles use DSRC (Dedicated Short Range Communications) to coordinate maneuvers, form platoons, avoid accidents (road casualties have a worldwide toll comparable to a war!). Coordination with pedestrians and bikers is the next step to improve Urban mobility and make smarter transportation in cities. The ideal candidate has a strong background in Computer Science, Networking, Simulation and Stochastic system, as well as an open mind-set to acquire novel competences in fields like automotive technology, electronics and human computer interaction.

C - Advanced techniques for the analysis of remote sensing images for cryosphere monitoring and hydrological parameter estimation (1 grant) 

The research activity that will be developed is related to the development of advanced methods for the analysis of remote sensing images. The methods should be able to automatically extract information from the satellite images and will be devoted to feature extaction and classification/decision fusion based on machine learning. The target data are both single and multitemporal SAR images as well as optical multispectral images. The techniques will be tested and validated on the problem of cryosphere monitoring and on the hydrologiccal parameter estimation.
The activity will be developed at the Remote Sensing Laboratory (RSLab) of the University of Trento.     
For more information on the activity of RSLab refer to: 
or contact Prof. Lorenzo Bruzzone

D - Distributed sensing and localisation for wheeled robots and human beings (1 grant)

Assistive robotics is an ever increasing research area nowadays, mainly due to the steadily growing of the aged population. Many national and European research initiatives focus on the development of solutions to prolong the independent living of elderly people. In this framework, solutions aiming at providing physical support and/or social engagement to seniors is becoming prominent. Planning and executing social activities with the physical support of (passive) service robots, such as intelligent wheeled walkers, become feasible once the group of seniors involved in the social activities can be accurately localised inside the desired environment. The goal of this research project, closely related to the EU project ACANTO, is to study effective solutions for human beings localisation inside instrumented environments, considering a single individual or a social group, which involves the development of proper human motion models.  Particular emphasis is devoted to localisation solutions based on (passive) service robots used to support elderly people in their navigation duties. The solutions provided could be extended during the research period to a broader class of wheeled robots, such as intelligent transportation systems. The research foreseen in this project will cover all the research and development phases, starting from the conception of the idea and the design of the solution to the experimental validation in real scenarios.

E - Crowd motion analysis and emergency management (1 grant) 

The objective of the scholarship is study, develop and test, innovative methods for crowd motion analysis, in terms of behaviour understanding and anomaly detection. The project will rely on the joint analysis of real and synthetic videos so as to model different scenarios, behaviours, in different contexts and environmental conditions.