10 Apr 2019 13:00

Borse di dottorato di informatica finanziate da IIT

La Fondazione Istituto Italiano di Tecnologia - IIT finanzia, per il dottorato di Informatica 35° ciclo, tre borse di dottorato sulle seguenti tematiche:

  • Structure from Motion and Time
    This research theme hasthe aim to provide a 3D reconstruction of complex historical urban environments. This will develop novel computer vision techniques able to handle complex and unique architectures built with a variety of materials as well as utilize a variety of data sources including historical archives. In addition to the Computer Vision advancements, potential Cultural Heritage/Digital Humanitiesapplications are related to:–detecting structural changes in the urbanscape through time–modelling the evolution of the urbanscape–detecting environmental variations across time.Methods to be investigated to this end may include multi-view methods for 3D reconstruction, machine learning approaches with deep architectures, such as point matching and image similarities estimation. This theme will be performed in collaboration with experts from Cultural Heritage/Digital Humanities, Machine Learning and/or Material Sciences.
  • Data Analysis Methods for Cultural Heritage
    The objective of this research theme is to develop novel data analysis algorithms to enrich the information and understanding of Computer Vision and/or Material Science applications in Cultural Heritage/Digital Humanities. The aim is todevelop novel methods based on deep learning, graph inference, multi-task learning, factorization and transfer learning, to enhance their effectiveness on the relevant applications, such as (but not limited to):-Knowledge Graph connectivity and decision inference-Temporal understanding of multi-modal data-Material chemical or visual property prediction.This theme will be performed in collaboration with experts from Cultural Heritage, Computer Vision and/or Material Science.
  • Deep Learning for Cultural Heritage
    This research theme will develop methods of inference across visual information, using novel approaches to identify Cultural Heritage/Digital Humanities assets. Such approacheswill enable to recognize Cultural Heritage objects, features,and semantic relationships between objects and features.This theme will be performed in collaboration with experts from Cultural Heritage and Machine Learning.

Approfondimenti ed infomaziani su tutte le borse di dottorato le potete trovare qui

Scadenza persentazione delle domande: 10 aprile 2019, ore 13


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