Skip to main content
Czech Republic

ICT International Doctoral School 2020 OPEN CALL – 29 PhD Positions


Research Field
Formal sciences
Natural sciences
Professions and applied sciences
Funding Type
Career Stage
European Research Programme
Not funded by an EU programme



All the details are available at:

Deadline for applications: 16th June 2020, 4 pm (Italian time, GMT +2)

The application must be completed and submitted by the above deadline, solely by the online system:

The application which you must select is "Doctoral School in Information and Communication Technology 36th cycle - First Call 2020"

Please note that applicants who do not have an account at the University of Trento must register in advance at:

The application shall be subject to the payment of an application fee - non-refundable - amounting to € 15 to be paid by credit card, according to the instructions given in the application.

During the filling out of the online application, applicants must choose which research area and no more than two project specific grants (reserved topic scholarships) within the area they are interested to apply for. 



Deep Learning per Human Behaviour Understanding (1 grant)

The research activities related to this PhD position will focus on the study and the implementation of visual data processing algorithms for the analysis of human behaviors in the context of Human Robot Interaction. In particular, the activities will focus on the development of algorithms based on deep learning for the detection of humans and the recognition of actions and activities in complex scenes. The developed algorithms will be integrated on a humanoid robot. The research activity is supported by the H2020 EU project SPRING.

Contact: Elisa Ricci

(Q@TN) - Hybrid Quantum-Annealer Algorithms for Tabu Search and Data-Driven Computation (1 grant)

Location: University of Trento, Italy in collaboration with German Aerospace Center (DLR) Köln, Germany

Description: The current availability of limited quantum hardware requires to devise hybrid quantum-classical algorithms that take advantage of the existing hardware and overcome their limitations by combining classical and quantum computation. One of the existing hardware, the D-Wave quantum annealer, solves optimization problems however its architecture suffers from limitations in the actual encoding of the target functions. A novel quantum-classical technique is based on a local search where already-visited solutions are penalized to avoid a redundant exploration of the solution space (so-called tabu search paradigm). Tabu search is implemented in the quantum annealer by a sequence of re-initializations of the Ising Hamiltonian of the qubit network iterated within the hybrid quantum-classical structure. An algorithm of Quantum Annealer Tabu Search (QATS) has been proposed in a recent paper (, D Pastorello, E Blanzieri Quantum annealing learning search for solving QUBO problems Quantum Information Processing 18 (10), 303, 2019.)

The PhD candidate will implement and test hybrid quantum-classical algorithms that can run on a quantum annealer. Initially, the PhD program will focus on the existing QATS algorithm. The expected result is a complexity-characterized, implemented and empirically evaluated tabu search quantum-classical algorithm for quantum annealers. In a second phase, the PhD activity will extend to the optimization of more general target functions and also towards data representation into the quantum annealer for data-driven computation.

Contact: Enrico Blanzieri

Human behaviour understanding(1 grant)

The research activity will focus on the study and the implementation of visual data processing algorithms for the analysis of human behaviors in the context of Human Robot Interaction scenario but also in the context of Smart City scenario. In particular, the activities will focus on the development of algorithms based on deep learning for the detection of humans and the recognition of actions and activities in complex scenes. The research activity is supported by the H2020 EU project SPRING in the context of Human Robot Interaction. The activities will also be part of the research done in the project MAE TALENT CUP 64I19002040001.

Contact: Nicu Sebe

Innovative methodologies for the tolerance analysis and the robust design of antenna systems with continuous and discrete apertures(1 grant)

The research activity will consist in the study, development, and numerical validation of innovative methodologies for the analysis of the tolerances of complex antenna systems having continuous and discrete aperture which allow obtaining inclusive and reliable bounds of the radiating performance. Moreover, integration of the tolerance analysis methodologies with suitable synthesis and optimization approaches in order to address the robust design of the antenna systems for next generation communications and sensing applications.

Contact: Andrea Massa

Advanced design of engineered electromagnetic materials for next generation radar and wireless communications systems (1 grant)

The research activity will consist in the study and development of innovative methodologies for the design of engineered electromagnetic materials able to manipulate the behavior of the electromagnetic waves in an unconventional manner in order to increase the performance of standard antenna systems and to provide innovative radiation features. The activities will focus on the exploitation and numerical validation of the innovative materials for next generation communications and sensing applications.

Contact: Giacomo Oliveri

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

Composition of the heterogeneous Data Bases (1 grant)

The project will concentrate on the definition of a general methodology and theory for the composition of heterogeneous ontologies.

Contact: Fausto Giunchiglia

(Q@TN) - Making Quantum Annealing Useful for Real: Compiling Effectively and Efficiently Very-Hard Combinatorial Problems into Ising Problems (1 grant)

Location: University of Trento, Italy, in collaboration with D-Wave Systems inc., Burnaby, Canada

We plan to investigate the usage of quantum annealers (QAs) “for real”, that is, to actually solve very-hard but relatively small SAT/MaxSAT problems (and eventually SAT/MaxSAT-encoded CSP/COP problems). The idea is to develop encodings from SAT/MaxSAT to Ising-minimization problems which fit into, and can be solved by, D-Wave’s Pegasus QAs (or other QAs, when/if available). These encodings must be performed both effectively (i.e., in a way that uses only the limited number of qubits and connections available within the QA topology, while optimizing the performance of the QA algorithm), and efficiently (i.e., using a limited computational budget for computing the encoding).

This work is intended as a follow up for an ongoing activity in collaboration with D-Wave Systems Inc. The PhD candidate initially will extend the approach we have begun with D-Wave’s Chimera topology. The main encoding scheme consists of a combination of offline and on-the-fly processes, the former performed by means of automated reasoning procedures (SMT, OMT), the latter by means of place-and-route procedures.

The encoded problems will be tested on D-Wave's new pegasus quantum annealers. The ultimate goal is to solve by QAs problems which are very challenging or even out of the reach of current SAT/MaxSAT tools. We envisage the possibility of an internship at D-Wave.

Contact: Roberto Sebastiani

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

Development of methodologies and automatic techniques for the analysis of data acquired by planetary radars (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 for more details on the mission).

Contact: Lorenzo Bruzzone

Development of methods based on artificial intelligence and machine learning for the analysis of remote sensing data acquired by planetary and/or Earth Observation missions (1 grant)

The research activity is devoted to the automatic analysis of satellite remote sensing data. Earth observation satellites and missions for planetary exploration acquire huge quantities of data (big data) by using different kinds of passive (e.g., multispectral and hyperspectral scanners) and active (e.g., synthetic aperture radar, radar sounder) sensors. The analysis of these data requires the use of advanced methodologies based on artificial intelligence and machine learning for the extraction of their semantic content. In this framework, the objective of the research is the definition, the implementation and the validation of deep learning methods for the analysis of remote sensing data. The main goal is to design methodologies that can exploit the properties of different kinds of remote sensing data in the processing and recognition tasks. The research activity can be focused on the analysis of either Earth observation data (and the related applications) or planetary data.

Contact: Lorenzo Bruzzone



Organisation name
University of Trento
Organisation Country
More Information



The responsibility for the funding offers published on this website, including the funding description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.