The Doctorate programme of the ICT School is organized in three curricula: Computer Engineering and Science, Electronics and Telecommunications and Industrial applications of ICT.

The research topics offered within these curricula encompass a number of modern research and technological problems related to information management, electronics and telecommunications. The curriculum in Computer Engineering and Science focuses on research topics concerning the fields of computer engineering and computer science. The curriculum in Electronics and Telecommunications focuses on research topics concerning the fields of electronics, measurements, telecommunications, electromagnetic fields and automation. The Industrial applications of ICT curriculum concerns all the scientific disciplines mentioned above but emphasizes the development of innovations and new knowledge meeting the needs of manufacturing. The research activity of the PhD students involved in this curriculum is necessarily accomplished within a three-year industrial project developed in collaboration with a supporting company.

The available research topics of PhD students are updated every year and are available at this page.

More details about the research areas involved in the Computer Engineering and Science and Electronics and Telecommunications curricula are provided below.

Smart sensors and innovative instrumentation

Applied researches focused design, development and characterization of sensors and instrumentation for automotive, agricultural, biomedical and industrial applications. Doctorate students involved will acquire deep knowledge and excellent ability in engineering and fundamental knowledge of measurement methods and techniques.

Brain-Inspired Computing

This research topic focuses on the development of new brain-inspired computing paradigms that can go beyond the traditional logic schemes of nowadays computers to realize high-performance and low-power Artificial Intelligence systems. Such platforms will exploit the properties of new non-volatile memory devices (such as Resistive, Phase Change, and Ferroelectric Memories) to implement synaptic behavior and learning possibilities directly on chip.

Big Logic-in-Memory Circuits

This research topic focuses on exploring emerging non-volatile memory devices (such as Resistive, Phase Change, and Ferroelectric Memories) as the building blocks for next-generation ultra-low power edge computing architectures. The aim is to concurrently exploit experiments and simulations to develop innovative solutions by implementing original device/circuit co-design strategies.

Compact Modeling of Emerging Non-Volatile Memories

This research topic is focused on the compact modeling of emerging non-volatile memory devices. The aim is to develop, test, and validate compact models that are based on the complex physics of such devices, yet show compatibility with state-of-the-art circuit simulation software. This will provide circuit designers with the appropriate tools for next-generation IC design, paving the way to tomorrow’s innovative circuit solutions.

Nanoscale small particle / single molecule bio-sensors arrays for health, food and environment

Detecting large statistical datasets of single particle/single molecule events is key to advance diverse fields of engineering for the health, food and environment sectors. Nanoscale sensing elements integrated in the back-end of low cost CMOS technology offer unique opportunities and the required sensitivity and settling time advantages compared to millimiter size companions. Our research is part of an international network involving NXP, TUWien, EPFL.

Iontronic and electronic sensor/actuator arrays for advancing brain studies and neuroscience

Neuron activity is an electrochemical process involving ion exchange through the neuron membrane and electrical pulse propagation. Ionic (as opposed to electronic) stimulation is an important ingredient of the neuron activity. Our research, part of the European project IN-FET, aims at developing mixed iontronic and electronic microtechnologies to stimulate and record neural activity, overcoming one of the major limitations of current microelectrode array methods.

Alternative semiconductor materials for More than Moore and More Moore nanoelectronics

Novel applications of nanoelectronics for IoT, energy, communication and quantum technology sectors, as well as innovation in mainstream CMOS technologies for integrated circuits, are nowadays relying on alternative, sometimes exotic materials possibly operated at cryogenic temperatures (e.g. graphene and 2D crystals , semiconducting oxides, IV-IV, III-V and II-VI semiconducting alloys, etc.). This research line aims at revealing and benchmarking potential advantages and limitations of new materials for nanoelectronics with emphasis on transport effects.

Vehicular networks and multiradio access technologies

Within the broad domain of wireless networking, the proposed research topics are framed in the setting of vehicular communications. They are centered on: (i) the design of new radio access schemes supporting superwideband, direct vehicle-to-vehicle communications at terahertz frequencies; (ii) the adoption of machine learning tools to predict the communication needs of vehicles in dense scenarios, subject to the constraint that large amount of data have to be delivered and shared among vehicles; (iii) the combined usage of multiple radio access technologies in infrastructure-based vehicular communications. All such cutting-edge themes allow PhD students to gain a significant experience on technologies and tools that will be instrumental in the design of future Intelligent Transport System (ITS) services for the automotive industry.

Innovative devices and circuits for next-generation hardware

This research area focuses on the development of innovative materials, devices, and circuits co-design. The aim is the development of next-generation low-power hardware to unleash the full potential of artificial intelligence and autonomous systems to the edge. Such systems will go beyond the traditional logic schemes of nowadays computers by exploiting the properties of new materials employed in emerging non-volatile memory devices (such as Resistive, Phase-Change, and Ferroelectric Memories) and innovative circuit schemes to implement features like synaptic behavior, learning possibilities, ultra-low power Boolean logic, and their synergistic integration. Doctorate students involved will acquire significant knowledge and ability in device and circuit engineering, fundamental knowledge of characterization techniques and modeling, and will be exposed to an interdisciplinary framework with contaminating elements from other science fields such as neuroscience.

Signal processing algorithms for wireless communication systems, radar systems and positioning systems

This research area focuses on the development and the implementation of innovative signal processing algorithms to be employed in different real world systems. These include wireless communication systems endowed with antenna arrays (i.e., multiple-input multiple-outuput, MIMO, communication systems), colocated MIMO radar systems and positioning systems based on the combination of heterogeneous technologies. Doctorate students involved in this research area acquire various skills; in particular, they learn how to develop novel algorithms for specific applications and analyse the problems originating from their implementation on commercial hardware.

Specialty optical fibers

This topic focuses on the development of innovative optical fibers for both scientific and engineering applications. These fibers, and in particular hollow core fibers, embody novel designs to overcome the main limits of standard optical fibers. They offer a new, flexible, and powerful platform for fields as diverse as fundamental physics, telecommunications, sensing, and laser technology and manufacturing. Doctorate students involved will acquire cutting-edge knowledge on photonics and optical fiber technology and design, and will join a collaboration network with prestigious European laboratories.

AI and Deep Learning for Computer Vision and Visual Understanding

Image processing and analysis and visual information retrieval from images and videos; multimedia and architectures for multimedia systems; computer graphics systems.

Big Data Management, Integration and Analytics

Methodologies and tools for the design and management of heterogeneous and distributed information systems. Automatic techniques to minimize the time that data scientists are spending preparing the data. Techniques for extracting aggregated summaries from big and heterogeneous data sources, performing entity recognition and resolution, dealing with their evolution. Development of ontologies and methodologies for the knowledge representation.

Machine Learning theory and applications

Machine learning is nowadays one of main pillars of modern AI systems. Learning from data means extracting, synthesizing and acquiring knowledge from soft/weak correlations among patterns that enables super-human capabilities in grasping and analyzing real life problems with automatic solutions. The activities focus, but are not limited to, applications and theoretical studies in the field of machine and deep learning, developing new learning algorithms, training methods and eventually enabling innovative applications.

Autonomous systems, Agents and Middleware

Methodologies and tools for high-degree distributed systems, in particular addressing collective adaptive systems, swarm robotics, software agents, mobile agents, pervasive computing. Main aspects: first, the software engineering techniques that support the development of complex systems; second, the coordination of the different components, at both model level and at infrastructure level. With regard to applications, different domains will be considered, e.g. as e-health, autonomous vehicles, intelligent manufacturing systems.

Cybersecurity

Any modern enterprise and organization should guarantee high performance, resiliency and security. The research area encompasses several related topics and applications, such as network and software security, cloud security, blockchain and smart contracts, intrusion detection systems based on machine and deep learning, production and protection of IoT and Industrial systems. The research activities have practical orientation thanks to the availability of large sets of real data and defensive systems that handle protection and valorization of continuously generated data

Fog and cloud computing

Management of cloud and fog computing platforms. The reference scenario is a distributed infrastructure where a set of sources produce data that are pre-processed by an intermediate layer of fog nodes before being sent and analyzed in cloud infrastructure. The research aims to tackle the problems of managing such infrastructure considering multiple aspects of the problem such as computing load in heterogeneous fog and cloud infrastructures, complex processing tasks, network characteristics, and power efficiency at the level of both fog and cloud.

Modeling, Identification and Control for Autonomous systems and Industrial Automation

This research area focuses on the development of novel modelling technique oriented to the control and suitable identification algorithms, possibly online, for parameters determination. This is the first step for the development of advanced control techniques able to conjugate model-based control with data-driven learning techniques, to be used both in an industrial field and in an unstructured environment, for robots, driverless vehicles and, more in general, autonomous systems. Ph.D. students involved in these research activities will gain significant knowledge on control tools and methods that will be instrumental in the design of future intelligent transport and production systems.