Reserved topic scholarships | Doctoral Program - Information Engineering and Computer Science
 

Reserved topic scholarships

Department of Information Engineering and Computer Science

B1 - Music and Artificial Intelligence (1 grant)

In this PhD position the candidate will work on novel AI methods applied to musical signals, for applications in intelligent musical interfaces, especially in networked settings. The successful candidate not only must have expertise with AI frameworks such as Tensorflow, but also substantial musical knowledge.

Contact: Luca Turchet luca.turchet [at] unitn.it

B2/B3 - Music in Augmented and Virtual Reality (2 grants)

In this PhD position the candidate will work on novel interaction paradigms within the domain of music performed, learned or experienced within virtual and/or augmented environments, especially in networked contexts. The successful candidate not only must have expertise with visual 3D software such as Unity 3D or Unreal, but also musical knowledge.

Contact: Luca Turchet luca.turchet [at] unitn.it

D3 - Development of techniques based on machine learning and signal processing for the design and data analysis of planetary radars (1 grant)

The research activities are related to planetary radar sounders, which are instruments for the study of the subsurface of the planets of the Solar system. These radars operate from satellite platforms and acquire data related to the subsurface of celestial bodies. The research will be focused on the development of a new generation methodologies for the design of radar sounders and/or for the analysis of their data. Special emphasis will be given to methodologies that exploit the most recent developments in the framework of machine learning and signal processing. Research will be developed at the Remote Sensing Laboratory (https://rslab.disi.unitn.it/) and will be related to the activities in progress on the Sub-surface Radar Sounder on board the EnVision mission to Venus of the European Space Agency (see https://sites.lesia.obspm.fr/envision/ for more details on the mission) and on the Radar for Icy Moon Exploration (RIME) on board of the JUpiter ICy moons Explorer (JUICE) of the European Space Agency (see https://sci.esa.int/web/juicefor more details on the mission).

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

D4 - Development of methodologies based on artificial intelligence and machine learning for the automatic analysis of remote sensing images (1 grant)

The research activities are related to the development of novel methodologies based on artificial intelligence and machine learning for the automatic analysis of images acquired by Earth Observation satellites. The research will be focused on the problems of the semantic segmentation (classification) and information extraction from optical (multispectral and hyperspectral) images acquired by last generation satellites with specific attention to the Copernicus programme. Typical challenges related to the analysis of big data from space will be addressed. Research will be developed at the Remote Sensing Laboratory (https://rslab.disi.unitn.it/) and will be linked to the project activities in progress on the aforementioned topics in the laboratory.

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

Fondazione Bruno Kessler (FBK)

A1 - AI at the edge: end-to-end neural networks for audio processing on IoT devices (1 grant)

Machine learning and deep neural networks are extensively and successfully used to process audio on powerful computers, while several problems still need to be solved for porting the technology on low consumption devices with limited resources (both in terms of computation power and memory size). Research is necessary to reduce the redundancy in neural models to make them portable into the internet of things framework. Along this line of research, the Ph.D. thesis will address the problem of end-to-end neural processing for audio classification, keywords spotting, and privacy-preserving audio processing on resource-constrained embedded devices, considering the trade-off between performance and energy efficiency. Advanced explorative research directions will consider how adapting continual learning techniques to low-power end-devices and if approaches such as collaborative machine-learning without centralized training data (i.e. federated learning) can help in privacy-preserving resource-constrained scenarios.

Contact: Alessio Brutti brutti [at] fbk.eu

A2 - Deep continual learning under scarce supervision (1 grant)

Supervised learning is a very popular mechanism to teach machines vision-based tasks and skills. Supervision, however, is a bottleneck for building generic machines that can operate across different contexts, environments and applications, while learning and improving their understanding seamlessly. Ideally, machines should develop their own creative strategies for using the sensed data and their experience to continually learn without humans at their side. The research activities related to this PhD position will focus on building machine vision algorithms to teach machines to seamlessly understand environments: by exploiting as little supervision as possible, by being independent of the sensor modality being used, and by updating their knowledge when ground truth information becomes incrementally available over time.

Contact: Stefano Messelodi messelod [at] fbk.eu

A3 - Distributed embedded AI for energy-efficient smart sensing in IoT (1 grant)

The Internet of Things (IoT), including smart objects, wearables, and wireless sensor networks, is becoming a key technology to enable applications and services in several domains. Ultra-low-power embedded devices are pervasive; novel embedded machine learning frameworks have been introduced. Thus, distributing intelligence at the edge is possible, opening exciting research scenarios spanning from novel, innovative hardware for always-on or event-based sensing up to deep learning solutions, federated learning, and continual learning fitting resource-constrained platforms. Motivated by the challenges of these research scenarios, the research aims to (i) define novel hardware/software approaches to optimize AI at the very edge on energy-efficient embedded devices, in particular for audio processing and/or computer vision; (ii) to explore the potential of distributing and fuse the intelligence in heterogeneous nodes of an IoT (iii) to demonstrate the advantages of the investigated approaches in real-world application scenarios, such as those of smart cities.

Contact: Elisabetta Farella efarella [at] fbk.eu

A4 - Flexibility and Robustness in Speech Translation (1 grant)

The need to translate audio input from one language into 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 are now required to be flexible and robust enough to operate in different application scenarios and diverse working conditions. On one side, the industry calls for key features like real-time processing, domain adaptability, extended language coverage and the capability to adhere to application-specific constraints (e.g. length or lip-synch constraints in the subtitling and dubbing scenarios). On the other side, society calls for new efforts towards inclusiveness with respect to specific categories and groups (e.g. gender-sensitivity, customization to the needs of impaired users). Both dimensions (industry and society) face the orthogonal challenges posed by the variability of audio conditions (e.g. background noise, strong speakers’ accent, overlapping speakers). 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.

Contact: Marco Turchi turchi [at] fbk.eu

B4 - Multi-objective optimization methods to support one-click deployments of EdgeAI application flows (1 grant)

Applications relying on the most modern sensing devices and technologies, also combining complex artificial intelligence tasks are now mainstream. The typical approach to enable intelligent applications is cloud-centric, meaning that the intelligence is hosted in the cloud infrastructure, the sensor data collected by some IoT devices. Shifting intelligence from the cloud to the edge of the network can offer different advantages such as reducing the required bandwidth and latency and also improving users’ privacy. However, reconfigure and deploy an end-to-end processing flow that involves the three aforementioned architectural layers (the cloud, the edge and embedded devices) poses major challenges: many different constraints and trade-offs must be addressed (latency, response time , bandwidth, energy consumption, computational power, computational precision, etc.) The subject of this Ph.D. is to investigate and propose novel optimization (such as e.g. pareto-based optimization) and assessment methodologies to efficiently sample such a complex design space in target application sectors such as home, industry, manufacturing, farming, etc. The reference technological environment covers (but are not limited to) embedded device software engineering (micropython, mbed OS, C languages and dialects, etc.), machine learning frameworks deployable on tiny devices (tinyML, tensorFlow lite, etc.), edge-based frameworks (eclipse Kura, edgeX Fundry, etc.) and cloud-based IoT platforms and services with AI support and components (MS Azure, AWS Greengrass, ThingsBoard, etc.).

Contact: Fabio Antonelli fantonelli [at] fbk.eu

C1 - Computational models for understanding and changing human behaviors (1 grant)

Several important problems in modern society, such as pollution and global warming, arise from the inability to achieve cooperation between individuals over a large scale. Recent research is providing a growing evidence of the power of social influence (i.e. peer pressure), in promoting cooperative behavior. This PhD has the goal of developing computational models for modeling human behavior and social interactions and of designing data-driven strategies and incentive schemes for promoting collaboration and cooperation. These approaches will be also compared with gamification strategies in the context of real-world experiments. The work of the student will be in collaboration between two research units of Fondazione Bruno Kessler, MobS (directed by Bruno Lepri) and MoDiS (directed by Annapaola Marconi).

Contact: Bruno Lepri lepri [at] fbk.eu and Annapaola Marconi marconi [at] fbk.eu

C2 - Engineering Game-based Motivational Digital System for Personalized and Cooperative Learning (1 grant)

Gamification principles have proven to be very effective in motivating target users in keeping their engagement within everyday challenges, including dedication to education, use of public transportation, adoption of healthy habits, and so forth. School closures due to the COVID-19 pandemic and thus the sudden change in the management of the students' educational pathways has opened up the need for methods and digital systems able to support teachers in defining educational content and objectives for their classrooms and to keep students engaged in their training path. The goal of this PhD Thesis is to investigate approaches, techniques and tools to design and release educational digital systems for personalized and cooperative learning plans. This will be done exploiting AI techniques for adaptive gamification and will support teachers in the process of defining and monitoring dedicated learning plans for their students. At the same time, it will facilitate learning, will encourage motivation and engagement, will improve student’s participation and cooperation, and will stimulate students to expand their knowledge through dedicated learning plans and personalized feedback.

Contact: Antonio Bucchiarone bucchiarone [at] fbk.eu

D2 - Advanced methodologies for radar and radar sounder image processing (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 radar and radar sounder images. The PhD activity will be developed in the context of European Space Agency (ESA) space mission JUpiter ICy moons Explorer (JUICE) in the Jovian system. The candidate will be requested to deal with images acquired from active radar systems including Synthetic Aperture Radar (SAR) images and sub-surface radar sounding data from airborne Earth Observation missions and satellite planetary exploration missions. The activity amins in improving the understanding of subsurface structure and their impact on planetary body climate. 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, deep learning, image/signal processing, statistic/remote sensing/radar.

Contact: Francesca Bovolo bovolo [at] fbk.eu

TIM S.p.A.

B5 - Functional modules for O-RAN architecture (1 grant)

The introduction of O-RAN allows the evolution of the Radio Access Network towards intelligence and openness. The research activity in this field is focused on the study of techniques for virtualization and artificial intelligence approaches to optimize radio access. For proper development of such research topics, it is considered relevant prior knowledge about mobile networks and optimization mechanisms.

Contact: Gian Michele Dell'Aera gianmichele.dellaera [at] telecomitalia.it and Fabrizio Granelli fabrizio.granelli [at] unitn.it

Accademia Europea di Bolzano (EURAC Research)

D1 - Snow cover detection and glacier mass balance estimation with machine learning methods and multi-source satellite data (1 grant)

Changes in glacier area, elevation and mass are major indicators for climate change and are identified as “Essential Climate Variables” by the World Meteorological Organization. This PhD project will focus on the development of machine learning methods for snow cover detection over glacierized areas and glacier mass balance estimation, exploiting different sources of satellite data. These will include high and medium resolution multi-spectral images (e.g. Sentinel-2 and Sentinel-3) as well as synthetic aperture radar data (e.g. Sentinel-1). The methodology will be designed and tested over in-situ monitored glaciers in Norway, Svalbard and European Alps. Then, the transferability to glacierized regions with less ground data available will be tested. The project will allow to build and automatically update a consistent time-series of glacier surface mass balance and area change. These are highly valuable data for the hydropower industry, governmental agencies and the research community, e.g. to improve runoff forecast for enhanced water management (e.g. drinking water, hydropower, agriculture) and to increase the knowledge about glaciers as climate variable, their fading in a warming climate and their contribution to global sea-level rise.

Contact: Mattia Callegari  mattia.callegari [at] eurac.ed and Lorenzo Bruzzone lorenzo.bruzzone [at] unitn.it