Reserved topic scholarships
Department of Information Engineering and Computer Science
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 e.ricci [at] unitn.it
A2 - (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 (https://arxiv.org/abs/1810.09342, 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 enrico.blanzieri [at] unitn.it
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 niculae.sebe [at] unitn.it
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 andrea.massa [at] unitn.it
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 giacomo.oliveri [at] unitn.it
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
The project will concentrate on the definition of a general methodology and theory for the composition of heterogeneous ontologies.
Contact: Fausto Giunchiglia fausto.giunchiglia [at] unitn.it
C2 - (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 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 roberto.sebastiani [at] unitn.it
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
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
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 lorenzo.bruzzone [at] unitn.it
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
The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In particular, current hate speech detection systems do not perform well on under-resource languages, are not able to generalise well across different platforms, and fail to integrate contextual information (e.g. network structure, discourse context, links to external media). We are therefore looking for candidates with strong interest in hate speech detection using deep learning techniques that would contribute to the development of novel approaches for robust hate speech detection designed to work in multilingual settings with small or no language-specific training data (zero-shot learning).
Contact: Sara Tonelli satonelli [at] fbk.eu
The need to translate the audio from one language into a text in a target language has dramatically increased in the last few years with the growth of audiovisual content freely available on the Web. Current speech translation (ST) systems need to be able to serve different applications working in various scenarios and to satisfy several factors coming from the market (e.g. specific length of the output, adaptation to different domains, real-time processing) or present in the audio (e.g. background noise or strong accent of the speaker). The objective of this PhD is to advance the state of the art in speech translation to make ST flexible and robust to these and other factors. Candidates should have a strong curiosity to solve problems in natural language processing and have a background in deep learning and maths, as well as excellent programming skills in Python. They will work both on theoretical aspects of the problem, and on their practical application in relevant case studies driven by ongoing projects where the MT unit is involved. Applicants are invited to contact us (turchi [at] fbk.eu and negri [at] fbk.eu) in advance for preliminary interviews.
Contact: Marco Turchi turchi [at] fbk.eu, Matteo Negri negri [at] fbk.eu
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
We are looking for a candidate willing to embark a challenging activity focused on the development of innovative Microsystems-based Radio Frequency RF-MEMS passive components and networks for next generation of telecommunications, wireless and radio systems and applications, like 5G and the Internet of Things (IoT). Capitalising on the fully in-house RF-MEMS technology, the candidate will have the opportunity to focus on different stages of prototypes development, from the elaboration of novel RF-MEMS design concepts, to the multi-physical simulation, modelling, fabrication, experimental testing of physical samples and integration.
Contact: Jacopo Iannacci iannacci [at] fbk.eu
Embedded systems are a fundamental component of our world. Their ubiquitous adoption and ever-increasing complexity makes the task of verifying their correctness both extremely challenging and extremely important. Techniques based on formal methods and automated theorem proving (particularly in the form of Satisfiability Modulo Theories - SMT) are very appealing in this context, promising to deliver both a higher level of confidence than traditional techniques and a high degree of automation. The objective of this PhD research is that of advancing the state of the art in the application of SMT-based formal methods to parameterized systems -- systems consisting of an unbounded number of components/processes -- which naturally arise in many safety-critical domains. The candidate will work on both theoretical aspects of the problem, as well as its practical applications in relevant case studies, drawn from the domains of railways, avionics and aerospace.
Contact: Alessandro Cimatti cimatti [at] fbk.eu, Alberto Griggio griggio [at] fbk.eu
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 radar images. The candidate will be requested to deal with images acquired from active systems including Synthetic Aperture Radar (SAR) images acquired from Earth Observation satellite missions, and sub-surface radar sounding data from airborne Earth Observation missions and satellite planetary exploration missions. The latter activity is developed in the framework of the Radar for Icy Moons Exploration (RIME) payload on board of European Space Agency (ESA) JUpiter ICy moons Explorer (JUICE) and Sub-surface Radar Souder (SRS) payload under development for the European Space Agency (ESA) Europe's Revolutionary Mission to Venus (EnVision). 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/radar.
Contact: Francesca Bovolo bovolo [at] fbk.eu
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