We encourage brilliant students who plan to get a master degree by October 2014 to apply to this program. The call for applications is athttp://www.unipd.it/en/node/
The PhD program has two curricula:
-- Neuroscience, Technology and Society (1 scholarship)
-- Computer Science for Societal Challenges and Innovation (5 scholarships, of which 3 with a specific theme)
The themes of the 3 scholarships are as follows.
Feel free to get in touch with the contact person if you are interested in any of these scholarships.
1. Theme: Smart and assistive technology
Contact person: Francesca Rossi, email@example.com
Funded by Fondazione Bruno Kessler.
Main topic: Modeling & aggregating preferences in collective systems
Collective systems comprise a large number of social and technical entities which are autonomous in terms of their goals and actions, but whose individual behaviors influence each other and define the overall performance of the system. An example is integrated urban mobility, where urban policies, available services and infrastructures, and individual citizens' behaviors all impact the overall performance. Decision making in collective systems is highly distributed and multi-level, and requires the capability to model and aggregate the preferences of all the encompassed entities. This topic investigates methods and tools for modeling and processing individual and aggregated preferences in the scope of collective systems.
2. Theme: Cloud computing
Contact person: Tulio Vardanega, firstname.lastname@example.org
Main topic: Service design in the Cloud
The research challenge addressed by this grant is the exploration of the design principles and implementation solutions capable of delivering low-latency low-footprint elastic capacity to application services deployed on the Cloud. The work work will be carried out from the angle of the logical (PaaS) and physical (IaaS) infrastructure underneath the application delivery service, with the intent of allowing flexible and opportunistic federation of selected elements of heterogeneous vertical solutions.
3. Theme: Bioinformatics for Personal Genomics
Contact person: Alessandro Sperduti, email@example.com
Main topic: Development and application of machine learning procedures for analysis of genomic data from patients with monogenic and oligogenic diseases.
The general aim is to develop suitable and flexible machine learning based methods and software tools for the characterization and classification of genetic data of individuals affected by a particular disease. The PhD student will work in a highly interdisciplinary research environment at the intersection among Computer, Biology, Medical and Cognitive Sciences. High performance computing facilities, as well as state-of-the-art genetic and medical data, will be available to the PhD candidate for the development of the targeted research activity.