Calls 2024

Publicly available multimodal datasets often consist merely of text captions paired with images, text with audio, or audio with images, while also merely providing descriptions of the images without establishing any deep-level connection between the text content and the visual or auditory content. Such formats fail to reflect the complexity of human perception, such as how we process images, listen to audio, or comprehend text. The PhD candidate will be tasked with exploring new modalities that more accurately reflect human vision and attention as they align with verbal descriptions. This initiative aims to foster a profound understanding of how visually perceived content (including images, videos, and text) is processed and understood by humans. Achieving this understanding will allow us to advance in the comprehension capabilities of Large Multimodal Models, while enhancing their ability to interpret and interact with a wide range of data.

3D scene understanding is an area of vision research with applications ranging from augmented reality to autonomous navigation. This PhD position is focused on research into foundational models for 3D scene understanding. 3D scenes can be created from image collections (Structure from Motion, Simultaneous Localisation and Mapping), thus allowing for the extraction of foundational representations from images and their transfer to the 3D domain using pixel-to-point correspondences. These representations can then be interacted with via language model prompting. However, these representations have been optimised for 2D reasoning. The PhD candidate will be tasked with exploring novel approaches to disentangle object-level information in the 2D domain and to fuse it in 3D, thereby enabling 3D reasoning capabilities.

The Internet of Things (IoT) paradigm proposes massive increases in multimodal data creation on tiny devices located at the far edges of the computing infrastructure. Despite the resource constraints of these devices, communication constraints require the data they produce to be locally processed before transmission. Further, due to the increasing complexity of the data generated, machine learning techniques are being successfully applied at the far edge through so-called TinyML. While most TinyML techniques focus on the individual device, increasingly these devices are both networked together and/or connected to larger cloud or swarm based infrastructures, introducing challenges for managing communication and coordination, both in data collection and usage as well as regarding elements of distributed training and continuous learning. Research into these challenges requires innovation in i) applying novel techniques such as distillation, hardware aware scaling and neural architecture search to implement orchestratable TinyML algorithms, ii) innovative communication, including optimizing the low power communication network connecting edge devices to the infrastructure, and iii) orchestration techniques required for ML-enhanced edge devices to participate in complex distributed applications formed of heterogeneous devices.

This interdisciplinary research at the intersection of Artificial Intelligence, Embedded Systems, Distributed Computing, and Low-power Hardware will take into account the candidate's profile and interests, contributing to the development of innovative solutions for real-world challenges. The candidate will have the opportunity to work with cutting-edge technology, gain valuable experience in interdisciplinary collaboration, and make significant contributions to the field of machine learning at the very edge.

In the context of the European Space Agency (ESA) mission JUpiter ICy moons Explorer (JUICE) to the Jovian system, and the development of the ESA EnVision mission to Venus, we seek for candidates willing do develop methodologies for radar sounder data processing (data enhancement, semantic segmentation, denoising, content based retrieval, target detection, multitemporal analysis, etc.). The outcome of this activity will contribute in improving the understanding of the subsurface of planetary bodies, the correlation to history and climate as well as to a better understanding of the Earth.

The candidate will be requested to design and develop novel methodologies within artificial intelligence framework (machine learning, deep learning, pattern recognition, etc.) for effective information extraction from radar sounder data.

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/Data Science, Mathematics or equivalents;

• knowledge in artificial intelligence, image/signal processing, remote sensing, radar remote sensing.

This scholarship is funded by project ASI-INAF n. 2023-6-HH.0 JUICE-RIME-E - “Missione JUICE - Attività dei team scientifici dei Payload per Lancio, commissioning, operazioni e analisi dati” — CUP no. F83C23000070005

The recent Earth Observation missions like (ESA Copernicus - Sentinels, ASI PRISMA and COSMO-SkyMed, and future IRIDE constellation) make available databases of long, dense and worldwide image time series. The data have complex spatio-spectro-temporal behaviors and variability, and they show irregularities and misalignments.

Candidates will be requested to develop novel methodologies within artificial intelligence framework (machine learning, deep learning, pattern recognition, etc.) for effectively and efficiently process image time series for semantic segmentation, target detection and change detection across multiannual series of data.

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/Data Science, Mathematics or equivalents;

• background in artificial intelligence, image/signal processing, remote sensing, passive/active sensors.

A Network Intrusion Detection System (NIDS) serves as the initial line of defence against network attacks that threaten the integrity of data, systems, and networks. Over recent years, Machine Learning (ML) algorithms have been increasingly used in NIDSs to detect malicious traffic due to their remarkable accuracy in identifying malicious network activity.

Nevertheless, ML algorithms are susceptible to Adversarial Machine Learning (AML) attacks, which aim to evade the NIDS with small perturbations of the attack network traffic. This vulnerability has particularly severe consequences, as adversarial attacks pose a substantial threat to overall network security.

While the majority of current research in the field of AML has been directed towards computer vision tasks like image classification and object recognition, there has been a notable increase in interest and activity within the cybersecurity domain. Nevertheless, several challenges persist in this domain, encompassing both performance-related issues and the practicality of applying these methods to real-world scenarios.

The primary objective of this PhD scholarship is to conduct cutting-edge research in the field of AML with a focus on enhancing cybersecurity defences. The selected candidate will explore innovative techniques and methodologies to detect, prevent, and mitigate AML attacks, thereby improving the robustness and resilience of ML-based cybersecurity systems.

The drive to establish a cloud-to-edge continuum, spanning multiple heterogeneous compute regions, is progressively eroding the boundaries of security perimeters, calling for a zero-trust approach to security, where nothing, not even inside an organisation’s network, can be implicitly trusted. In such a scenario, a resource- and energy-efficient, opportunistic monitoring of users, services, platform, and infrastructure becomes paramount to enhance management and security in cloud-to-edge environments.

Monitoring and auditing have received increasing attention from research and industry in the recent years. Crucially, novel technologies have emerged to programmatically gather information from network flows, system calls, and other sources. These technologies, including eBPF (extended Berkeley Packet Filter) and P4, fall under the umbrella of the so-called programmable data planes.

The objective of this PhD endeavour is to design, implement and evaluate novel monitoring solutions capable of opportunistically diving into the appropriate depth of data collection, defining the right mix of data and user/control plane functions and their location. Additionally, they should be programmatically tailored to serve security and data analysis applications, while being suitable for dynamic scaling and orchestration. The overarching goal is to contain their footprint while delivering the required information effectively.

Programmable Data Planes (PDPs) offer the ability to customise and control the processing of network packets, either within network devices such as routers, switches, and SmartNICs, or within end-host machines, through technologies like eBPF (extended Berkeley Packet Filter), or other programmable frameworks. PDPs empower developers to define and implement customised packet processing logic, spanning from fundamental packet filtering and forwarding to more sophisticated tasks like load balancing, network virtualisation, and security enforcement.

While they promise to deliver enhanced flexibility and performance to processes and services, the capabilities of PDPs are still largely under exploration, proof, and assessment. As a matter of facts, due to design and implementation decisions, they are often restricted in the range and types of operations they can perform on packets. This thesis aims to delve into novel and advanced methodologies for offloading complex tasks, particularly those focused on security such as cryptography, traffic analysis, and filtering, onto PDPs, with the objective of striking the right balance between performance and complexity. The candidate is expected to analyse alternative solutions where tasks can be either entirely or partially offloaded and conduct experimental assessments, comparing the outcomes against legacy approaches.

This initiative contributes to global food security and environmental sustainability, fostering a collaborative and innovative research atmosphere focused on integrating data science and artificial intelligence (AI) with agriculture. It addresses urgent challenges such as resource management, crop yield optimization, and the impacts of climate change. As the global demand for food increases, this scholariship promotes the adoption and creation of technologies and methodologies to boost agricultural efficiency and sustainability. The successful candidate will engage in developing innovative, data-driven methods for sustainable agricultural practices. Additionally, he/she will explore interdisciplinary research, utilizing remote sensing, machine learning, and predictive analytics to produce actionable insights aimed at enhancing agricultural productivity and resilience. This scholarship aims to cultivate a new breed of researcher, adept at leveraging data intelligence for agricultural progress, with a focus on optimizing water usage, enhancing crop resilience, and implementing sustainable farming practices.

The proliferation of Internet of Things (IoT) devices and the increasing reliance on edge computing paradigms have ushered in a new era of distributed computing, where processing occurs closer to the data source rather than in centralized data centers. This shift promises to reduce latency, minimize bandwidth usage, and ensure data privacy and sovereignty. However, it also introduces significant challenges in managing, deploying, and maintaining machine learning (ML) models across a vast, heterogeneous, and geographically dispersed infrastructure. This doctoral research aims to address these challenges by advancing the integration of Machine Learning Operations (MLOps) practices within edge computing and IoT environments, facilitating the seamless deployment, monitoring, and management of ML models at the edge.

Techniques based on formal methods for the verification and validation of embedded and safety-critical software systems are becoming increasingly important, due to the growing complexity and importance of such systems in every aspect of modern society. Despite the major progress seen in the last twenty years, however, the application of formal methods in embedded software remains a challenge in practice, due to factors such as the interplay between computation and physical aspects and the increasing complexity of the software and its configurations.

This project will investigate novel techniques for the application of formal methods to the design, verification, and validation of embedded software, with particular emphasis on safety-critical application domains such as railways, automotive, avionics, and aerospace. The techniques considered will include a combination of automated and interactive theorem proving, satisfiability modulo theories, model checking, abstract interpretation, and deductive verification. Examples of the problems tackled during the project include the formal verification of functional requirements expressed in temporal logics, automated test-case generation, efficient handling of parametric/multi-configuration software systems and product lines, and the verification of software operating in a physical environment, subject to real-time constraints. Importantly, in addition to researching novel theoretical results, a significant part of the project activities will be devoted to the implementation of the techniques in state-of-the-art verification tools developed at FBK and their application to real-world problems in collaboration with our industrial partners.

Industrial systems are reaching an unprecedented degree of complexity. The process of designing a complex system is expensive, time consuming and error-prone. Moreover, the design process has to guarantee not only the functional correctness of the implemented system, but also its dependability and resilience with respect to run-time faults. Hence, the design process must characterize the likelihood of faults, mitigate possible failures, and assess the effectiveness of the adopted mitigation measures.

Formal methods have been increasingly used over the last decades to deal with the shortcomings of designing a complex system. Formal methods are based on the adoption of a formal, mathematical model of the system, shared between all actors involved in the system design, and on a tool-supported methodology to aid all the steps of the design, from the definition of the architecture down to the final implementation in HW and SW. Formal methods include technologies such as model checking, an automatic technique to symbolically and exhaustively analyze all possible executions of the system in the formal model, in order to detect design flaws as early as possible. Model checking techniques have been recently extended to assess the safety and dependability characteristics of the design, and for system certification.

The objective of this study is to advance the state-of-the-art in system design using formal methods. This includes adapting and extending the system design methodology, investigating improved versions of state-of-the-art routines for verification and safety assessment of complex systems, and developing novel extensions to address open problems. Examples of such extensions include novel techniques for contract-based design and contract-based safety assessment, advanced techniques for formal verification based on compositional reasoning, the analysis of the timing aspects of fault propagation, the characterization of transient and sporadic faults, the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns, and the modeling of analysis of systems with continuous and hybrid dynamics.

This study will exploit the challenges and benchmarks defined in various industrial projects carried out at FBK.

Nowadays, foundation models have shown remarkable capabilities in generating text, images and videos, rich world knowledge, and some complex “reasoning” skills. However, these models are still passive models (e.g., action-oriented aspects of intelligence are not leveraged), static, data- and computation-expensive, they often confabulate, and are difficult to align with human values.

This PhD project aims at making multimodal foundation models capable of interactions with other agents (e.g., enabling cooperative capabilities) and of interactions grounded in the world, enabling counterfactual reasoning and causality abilities in foundation models as well as enforcing their alignment with human intentions and values. The PhD candidate is required to have previous experience in working with deep learning algorithms, a strong interest on transformers’ architecture, graph neural networks, multimodality, and cooperative and embodied AI. The selected student will be able to collaborate with the ELLIS network and being part of the ELLIS PhD program (if selected) as well as with top universities and research centers such as MIT, Max Planck, etc.

Predicting individual and collective human behavior is crucial to address complex societal challenges. Recent research has focused on deep learning models for forecasting future behavior. While these models achieve impressive results, they face limitations:

limited generalizability, low interpretability, and difficulties in geographic transfer. This PhD project aims to design the next generation of computational models for understanding individual and collective human behavior. Social science research on social learning,

collective intelligence, and crowd wisdom identifies potentially generalizable behavioral patterns. Additionally, recent advancements in AI offer foundation models capable of reasoning and generalization.

The ideal candidate possesses a strong interest in a multidisciplinary approach encompassing machine learning (deep learning and foundation models), social sciences, urban mobility, and related fields. The ultimate goal is to contribute to the development of the first foundation model for human behavior.

The collaborative nature of the project fosters engagement with leading national and international universities, creating a dynamic research environment.

Digital twins are dynamic and self-evolving models that simulate a physical asset and represent its exact state through bi-directional data assimilation. They employ Artificial Intelligence (AI) data-driven and symbolic techniques to provide state synchronization, monitoring, control and decision support.

This project will build on the results of the ongoing ESA-funded project ExploDTwin ("Digital Twin for Space Exploration Assets"), which aims at integrating the DT into the ESA infrastructure to support online space assets operations with functionalities such as planning, what-if-analysis, fault detection, diagnosis and prognosis. To this end, ExploDT introduces a model-based design methodology that allows to seamlessly integrate engineering methods and AI techniques into a cohesive DT framework.

The PhD student will investigate new formal methods to analyze the DTs by integrating model checking, automated theorem proving, simulation, and machine learning. Different aspects of the DTs will be considered including temporal properties for validation, monitorability and diagnosability. The new methods will be implemented and evaluated on space related benchmarks derived from ExploDTwin results.

Point clouds are nowadays an indispensable tool in the heritage field, but their actual usage by non-experts engaged with preservation and restoration challenges is often very limited, due to low explainability, human-readability, accessibility and data integrability issues. All of these issues fall under the conceptual umbrella of “understandability”.

AI-based approaches particularly suffer these problems, but, as point clouds, they are an indispensable tool for digital heritage. Other approaches, e.g. based on 3D ontologies, could support understanding aspects but they have been limited addressed.

Therefore the goals of the proposed PhD are:

(i) To study, develop and validate generalisable ontology-based approaches to facilitate the query and use of large and complex 3D heritage point clouds by means of rules able to infer properties and characteristics of a surveyed scene

(ii) To conduct research on novel ways to integrate formal ontologies and AI-based methods to support explainability

(iii) To integrate LLM and NLP models to support 3D heritage understanding

The successful candidate is supposed to have a good ability to connect ICT/AI solutions with heritage needs, along with the agility to successfully prototype innovative, reliable and replicable software solutions.

Biodiversity is under threat from habitat loss and fragmentation, climate change, extreme land use and invasive alien species. Biodiversity monitoring for conservation purposes using geospatial data has seen some progress in recent years. Traditional survey methods are time-consuming, labour-intensive and require skilled staff. Surveying can be challenging, especially to keep track of rare or elusive species living in inaccessible or dangerous areas. Photogrammetric and LiDAR data has led to important advancements and impacts on ecosystem understanding as they enable precise assessments of habitat structures, mapping of species distributions or ecosystem dynamics, all essential information for conservation efforts. Nonetheless, better objective processes and monitoring solutions, with new tools to enhance and enrich current practices and assist ecologists, are needed.

Therefore, the goal of the interdisciplinary PhD is to:

(i) create and validate processes, based on 3D remote sensing data (airborne/drone monochromatic/multispectral LiDAR, photogrammetric point clouds, aerial/drone hyperspectral images, etc.) and AI methods, to locate and study various species, either animal or vegetal

(ii) use ground robotics platforms and sensors for terrestrial biodiversity exploration and monitoring in challenging environments

(iii) find biodiversity patterns, through multimodal data analytics, detecting hotspots of current issues, their trends and emerging threats

(v) combine/fuse 3D data to cross-validate the methods

(vi) find new links within geographical data between animals and vegetation.

In recent years, Multi-Modal Learning (MML) has garnered significant attention, driven by the increasing availability of vast multimodal datasets and the development of robust internet services accessible across various devices. Current research predominantly emphasizes the imperative to harness deep learning techniques, capitalizing on existing foundational models applicable across diverse domains, and customizing them for specific tasks within the MML framework.

The primary objective of this thesis is to advance the state of the art in MML by delving into cutting-edge neural architectures and learning approaches. This involves the exploration of innovative methods, potentially combining different modalities such as voice and gestures. The aim is to enhance the performance of existing single-modal systems, particularly in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP).

A pivotal focus will be on investigating recent audio foundation models, specifically those designed for multi-lingual speech recognition, voice conversion, and speech generation to facilitate data augmentation. The overarching goal is to develop models that exhibit effectiveness across various speech tasks, even when confronted with limited data or constrained computation resources. Furthermore, through the integration of advanced audio generation techniques, the study seeks to bolster the multimodal capabilities of the overall system. This enhancement enables more effective creation of synthetic data for model training and development.

The outcomes of this research are anticipated to contribute significantly to the development of highly competitive services tailored to the demands of evolving multimodal application scenarios.

The advent of foundation models has led to impressive advancements in all areas of natural language processing. However, their huge size poses limitations due to the significant computational costs associated with their use or adaptation. When applying them to specific tasks, fundamental questions arise: do we actually need all the architectural complexity of large and - by design - general-purpose foundation models? Can we optimize them to achieve higher efficiency? These questions spark interest in research aimed at reducing models’ size, or deploying efficient decoding strategies, so as to accomplish the same tasks while maintaining or even improving performance. Success in this direction would lead to significant practical and economic benefits (e.g., lower adaptation costs, the possibility of local deployment on small-sized hardware devices), as well as advantages from an environmental impact perspective towards sustainable AI. Focusing on automatic translation, this PhD aims to understand the functioning dynamics of general-purpose massive foundation models and explore possibilities to streamline them for specific tasks. Possible areas of interest range from textual and speech translation (e.g., how to streamline a massively multilingual model to best handle a subset of languages?) to scenarios where the latency is a critical factor, such as in simultaneous/streaming translation (e.g., how to streamline the model to reduce latency?), to automatic subtitling of audiovisual content (e.g., how to streamline the model without losing its ability to generate compact outputs suitable for subtitling?).

The growing complexity of software systems requires the development of new methods and tools to design and test software systems characterized by high variability in the space of possible functional configurations and possible release architectures. The objective of this doctoral thesis is to explore new approaches to testing, verification and validation of this type of complex systems involving the joint use of model-based and artificial intelligence techniques such as optimization, planning and machine learning.

Microfluidics is a branch of microsystem with great potential to integrate complex analytical procedures into a small device, which allows the detection of biological and chemical analytes in portable and automated systems. Among different techniques, the recent trend is to move from classical but miniaturized pressure-driven devices to droplet techniques and digital microfluidics, which allow a better miniaturization and integration of different functionalities without an increase of the technological complexity. In particular, the Electrowetting On Dielectrics (EWOD) is a droplet-based technique, where the sample and reagents are managed by modulating the wetting properties of a surface by electric fields.

The candidate will participate in a research team developing EWOD devices for portable analytical systems for the analysis of water quality and other applications. The activities will include the development of MEMS technologies, the design and the experimental validation of the devices.

Accessing advanced acceleration structures with microfabrication techniques: Particle accelerators are becoming huge - the proposed Future Circular Collider with 100km circumference. Here, acceleration is addressed from the opposite end, using microfabrication to obtain extremely compact structures, to produce particle beams for a variety of possible applications. In other words: particle acceleration on a chip.

The topic will study and design possible devices. It will then utilise the micro- and nano- fabrication facilities at the Sensors&Devices to realise them and to test these prototype structures. Consideration of potential applications of such devices will be considered from the beginning of the design.

An interest and aptitude for design and microfabrication of silicon devices is expected. A hands on and practical mentality is desired. Experience of data handling and analysis is similarly helpful. An interest in the particle acceleration and applications of particle beams is useful.

Development of single or few-qubit gates for integrated quantum photonic circuits. The PhD will address the fundamnetal theoretical aspects, design, fabrication and characterisation of integrated chip-scale devices.

The LHC experiments produce about 90 petabytes of data per year. Inferring the nature of particles produced in high-energy collisions is crucial for probing the Standard Model with greater precision and searching for phenomena beyond the Standard Model. In this context, event selection is becoming more difficult than ever before and requires expertise at the border between physics and computer science. During the PhD the student will be guided in exploring and designing algorithms that mix background knowledge with Deep Learning to tackle this problem, learning to apply rigorous Data Science methodologies. The activity will be carried out in collaboration with INFN-TIFPA, Fondazione Bruno Kessler, and within the ATLAS experiment at the LHC. Candidates familiar with High Energy Physics are welcome, and basic knowledge of Machine Learning/Deep Learning is recommended.

Silicon Carbide (SiC) has emerged as a promising semiconductor material for electronic devices due to its exceptional material properties, including high thermal conductivity, wide bandgap, and excellent chemical and mechanical stability. As SiC-based devices become increasingly integral to various applications, understanding and optimizing dopant characteristics is crucial for advancing their performance and reliability. This thesis focuses on the comprehensive characterization of dopants in SiC semiconductors, aiming to bridge the gap between material science and device engineering. The research involves the exploration of various dopant types, concentrations, and their impact on the electrical, structural, and thermal properties of SiC. Both traditional and novel doping techniques are investigated, with an emphasis on their effects on carrier mobility, doping efficiency, and device functionality. The experimental methodology encompasses advanced analytical techniques such as Secondary Ion Mass Spectrometry (SIMS), X-ray Photoelectron Spectroscopy (XPS), and Hall effect measurements to precisely quantify and analyze dopant profiles, dopant activation levels, and their distribution within the SiC lattice. Additionally, the impact of dopants on defect formation and migration in SiC will be investigated to gain insights into the material's long-term stability and reliability. The research also explores the practical implications of dopant characterization on SiC device performance. Through the integration of the acquired knowledge, this study aims to propose optimized doping strategies for enhancing the efficiency and reliability of SiC-based electronic devices. The findings of this research hold significant potential for advancing the field of SiC semiconductors, enabling the development of next-generation electronic components with improved performance, efficiency, and longevity.

Tip-enhanced Raman Spectroscopy (TERS) combines the chemical analysis of the Raman technique with the increased sensitivity of the SERS (Surface Enhanced Raman Spectroscopy) approach and the nanometric spatial resolution of the Scanning Probe Spectroscopy (SPM). Since the technique uses a Raman spectrometer, Tip-Enhanced Photoluminescence (TEPL) is also possible for probing chemical, electrical and optical properties at the nanoscale. The spectroscopy information is provided along with all the measurements enabled by SPM, such as topography and electrical and mechanical properties. The advantages of these spectroscopies, namely the lateral resolution beyond the diffraction limit and the non-destructive, label-free and in-air interaction, open new possibilities for nanotechnology and quantum applications. A new TERS/TEPL equipment was recently installed in the FBK-SD laboratories. This PhD project aims to set up the TERS/TEPL techniques and apply them to the study of optical and morphological properties of semiconductor materials, also nanostructured, used for quantum technologies, down to the single quantum object scale. This research activity will be carried out in the framework of some ongoing projects on quantum sensors and devices at FBK-SD.

We are seeking a PhD candidate interested in working at the design and experimentation of AI-powered digital interventions for the prevention and management of chronic diseases. The interventions will be based on evidence-based approaches and protocols for the treatment of these conditions. Background expertise on human-centered design methods, psychology and computer science are welcome to inform the design of the digital interventions.

The PhD candidate will both investigate and contribute to advancing the state of the art of gamification approaches in the design of the user engagement with digital therapeutics for mental well-being. The intervention design will be based on evidence-based protocols for mental health and well-being as active ingredients of the treatment, as well as different types of excipients for the intervention (conversational agents, VR environments, serious games, etc.). Background expertise in the areas of HCI, psychology and computer science is welcome.

We seek a candidate for a PhD position in neuro-symbolic AI, with expertise in both neural networks and symbolic reasoning techniques. Neuro-symbolic AI refers to hybrid approaches to artificial intelligence that combine machine learning (and in particular deep neural networks) with symbolic reasoning techniques. These approaches aim to leverage the strengths of both paradigms: neural networks excel at learning patterns and extracting features from large datasets, while symbolic systems can provide abstract interpretable representations and logic-based or algorithmic reasoning. The candidate will have to study and develop neuro-symbolic systems that address some limitations in current AI systems, such as the difficulty in generalizing knowledge across different domains or tasks. This may involve designing novel architectures that integrate neural and symbolic components, developing algorithms for learning and reasoning with hybrid representations, and exploring new applications where neuro-symbolic approaches can offer some advantages over purely neural or symbolic methods.

The PhD candidate will focus on the development of superconducting quantum devices, such as superconducting qubits and parametric amplifiers, and on the optimisation of the circuit components, such as Josephson junctions and high-kinetic-inductance components. The candidate will be involved in the design and simulations as well as in the microfabrication and in the cryogenic characterisation of the devices. The produced devices will be exploited to investigate fundamental physics cQED processes in the microwave domain and to be integrated in complete quantum systems.