Reserved topic scholarships

NB: Reserved topic scholarships of the 1st Call 2020 can be found HERE

Department of Information Engineering and Computer Science

A1 - Optimization Modulo Theories (1 grant)

The research activity will aim at developing novel techniques, methodologies and support tools advancing the state of the art in Optimization Modulo Theories (OMT), in particular - but not restricted to:

  • extending OMT with optimization with non-linear constraints and objectives
  • extending OMT for dealing with Constraint Programming in MiniZinc.

All such tools will be implemented inside the OptiMathSAT OMT solver (http://optimathsat.disi.it/) developed in Trento on top of the MathSAT.5 SMT platform (http://mathsat.fbk.eu/). The research will also aim at investigating novel OMT applications in various fields including - but not restricted to - Formal Verification, Artificial Intelligence and Quantum Computing.

Contact: Roberto Sebastiani roberto.sebastiani [at] unitn.it

A2 - Distributed control (PRIN 2017 - CUP: E64I19002520001) (1 grant)

The PhD will develop research on distributed control solutions for systems and networks.

Contact: Luigi Paolopoli luigi.palopoli [at] unitn.it, Daniele Fontanelli daniele.fontanelli [at] unitn.it

A3 - Low-power Localization for the Internet of Things (IoT) (1 grant)

Research will focus on protocols and techniques to perform distance estimation (ranging) and localization in IoT scenarios using low-power radios. The main technology considered will be ultra-wideband (UWB) radios, expected to become widespread in the near future, as witnessed by their inclusion on Apple’s iPhone 11. However, the proposed research will also explore integration with other types of low-power radios offering complementary characteristics such as very low power (e.g., Bluetooth 5) or very long-range (e.g., LoRa).

Contact: Gianpietro Picco gianpietro.picco [at] unitn.it

B1 - Ultrasound Imaging Solutions Dedicated to Bubbly Media​ (1 grant)

Ultrasound Medical Imaging is a widely employed diagnostic technology. To give a few examples, it is utilized to visualize internal body parts non invasively, to quantify perfusion, to assess blood flow, and to characterize tissue. Despite the already humongous range of applications, we have yet not completely revealed the full potential of this safe, non invasive, cost effective and portable technology. This is especially true in the context of bubbly media, where the volume of interest is in great part occupied by gas. 
Practical examples are Contrast Enhanced Ultrasound Imaging (of great interest for cancer detection and localization), and Lung Ultrasound.  
This project will focus on the development, implementation and testing of ultrasound imaging solutions dedicated to bubbly media. The candidate is expected to have a background is signal processing, image formation, and image analysis. Background in ultrasound imaging or medical image processing is considered a plus. 

Contact: Libertario Demi libertario.demi [at] unitn.it

B3 - Development of methodologies and automatic techniques for the analysis of data acquired by planetary radars [additional] (1 grant)

The research activities are related to planetary radar sounders that are instruments for the study of the subsurface of the Earth and planets. These radars operate from satellite platforms and acquire data related to the subsurface. The research will be focused on the development of a new generation of simulation and analysis techniques for planetary data that exploit the most recent developments in the framework of artificial intelligence and deep learning. The activity will be related to the definition, design, implementation and validation of:

  • Radar simulation and signal processing algorithms based on deep learning techniques.
  • Data analysis techniques based on artificial intelligence for the automatic extraction of the semantic from the data; 

Part of the research will be related to the activities in progress on the development of the Sub-surface Radar Sounder under study in the framework of the EnVision mission to Venus of the European Space Agency (see https://envisionvenus.eu/envision/ for more details on the mission). 

Contact: Lorenzo Bruzzone lorenzo.bruzzone [at] unitn.it

Fondazione Bruno Kessler (FBK)

A4 - Data-driven techniques for closed-loop network and service management and RAN disaggregation in 6G Mobile Networks (1 grant)

Although 5G has just arrived, the research towards 6G mobile networks has already started. 5G paved the way towards a connected world where multiple verticals (e.g., automotive, industry, and health), each characterized by different performance targets in terms of bitrate, latency, and reliability, can coexist on the same infrastructure. 6G networks will push this paradigm even further and will require a paradigm shift in the way mobile networks are deployed and operated.

With this fully-funded PhD position, we are looking for a candidate willing to work on cutting edge research in the field of 6G mobile networks with a particular focus on data-driven techniques for closed-loop network and service management and RAN disaggregation (following O-RAN principles).

The successful candidate has obtained a master's degree with excellent marks in computer science, is proficient in networking and programming, has an affinity for algorithm design and artificial intelligence, and enjoys working in a multi-disciplinary project. In particular, evidence of system research experience (that is building your own prototypes to validate fundamental research results) using open-source software like srsLTE and P4 is a strong advantage.

Contact: Roberto Riggio rriggio [at] fbk.eu

A5 - Deep Learning Models for Human Behaviours [additional] (1 grant)

This PhD project has the ambition to explore the fusion of multiple modalities and the design of novel cross-modal deep neural network architectures to study social behaviours, social interactions, and human activities In addition, the candidate will work on Generative Adversarial Network (GAN) models to generate realistic human behaviours in a variety of social settings. The ideal candidate will be strongly motivated in developing skills in machine learning with a special focus on deep learning, and in computer vision, multimodal approaches, and human behaviour analysis. The project will be supervised by Bruno Lepri (FBK) and Nicu Sebe (DISI).

Contact: Bruno Lepri lepri [at] fbk.eu, Nicu Sebe niculae.sebe [at] unitn.it

 

B2 -  Multi-/Hyper-temporal remote sensing image time series analysis (1 grant)

We are looking for candidates willing to develop novel methodologies based on machine learning, deep learning pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in multi-/hyper-temporal remote sensing images.The candidate will be requested to deal with both multi-/hyper-spectral images acquired by passive satellite sensors and Synthetic Aperture Radar (SAR) images acquired from active systems for Earth Observation. The goal is to design novel methods able to use temporal correlation to model landcover behavior, changes and trends for a better understanding of phenomena over time and of climate change. Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are: • master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents; • knowledge in pattern recognition, image/signal processing, statistic/remote sensing, passive/active sensors.

Contact: Francesca Bovolo bovolo [at] fbk.eu