Research Theses

Themes for the 4 scholarships without specific topic

a	Autonomic computing for collective self-adaptive systems
	Keywords: Autonomic computing, distributed systems, adaptive systems, IoT, autonomous vehicles Research objectives: Collective adaptive systems are more and more spreading in our life. Situations like sets of mobile phones or IoT devices are becoming real and can be exploited to support human activities, but this requires appropriate approaches to be managed; in the future we envision sets of autonomous vehicles that must be coordinated. Autonomic computing is a very good candidate paradigm to address these scenarios. The objective of the research is to define a framework for the development of collective self-adaptive systems, based on autonomic computing. The framework will be composed of a methodology that guides the developers in the development addressing the self-* properties, and of tools enabling the support for the developers. Some case studies will be proposed to test the framework. Proposed research activity: • State of the art in autonomic computing • State of the art in collective adaptive systems • Definition of a framework for autonomic computing in collective adaptive systems • Definition of a methodology • Definition of case studies • Test of the framework • Participation to relevant international school Supporting research projects (and Department) H2020 – FIRST (FIM)   Possible connections with research groups, companies, universities. Dr. Antonio Bucchiarone, FBK Trento (I) Dr. Lai Xu, Bournemouth University (UK) Prof. Emma Hart, Edinburgh Napier University (UK) Prof. Marco Aiello, Stuttgart University (D) Supervisor: Prof. Giacomo Cabri
b	Software engineering for autonomous vehicles
	Keywords: Software engineering, autonomous vehicles, distributed systems, adaptive systems, IoT Research objectives: Autonomous vehicles are spreading and more and more research is needed to enable their engineering. In particular, both the single vehicle and the coordination of sets of vehicles rely on software components that must be designed, implemented and verified; the current methods and methodologies could not be suitable for this new scenario. Appropriate approaches are needed. The objective of the research is studying the (meta)requirements of the development of software components and systems for autonomous vehicles, in order to define one or more approaches that are suitable for this scenario. Proposed research activity: • State of the art in software engineering • State of the art in autonomous vehicles • Definition of approaches to engineer the development of software components and systems for autonomous vehicles • Definition of a methodology • Definition of case studies • Test of the proposed approaches • Participation to relevant international school Supporting research projects (and Department): WASABI 2023 (FIM) Possible connections with research groups, companies, universities: Dr. Antonio Bucchiarone, FBK Trento (I) Prof. Emma Hart, Edinburgh Napier University (UK) Prof. Marco Aiello, Stuttgart University (D) Supervisor: Prof. Giacomo Cabri
c	Efficient confidential and verifiable data management strategies
	Keywords: Cyber security, Applied cryptography, Databases, Data structures Research objectives: Emerging security solutions try to minimize systems attack surface and mitigate information leakage even in presence of partially compromised systems. Among those, promising advanced cryptographic strategies are being studied to enable efficient query execution on encrypted databases and to verify correct execution of queries on data. These security solutions help mitigating data breaches perpetrated by external adversaries or even worse by legitimate insiders, and enable strong auditing strategies for outsourced data managed by external parties. The research activity focuses on studying state-of-the-art for database security and encryption, analyzing practical techniques for achieving practical performance and acceptable security, designing engineering strategies to embed these techniques within real-world database systems. Proposed research activity: • State of the art on database encryption and verification • Analyzing trade-offs in terms of security and performance • Designing novel strategies for improving security and performance of encrypted data • Evaluating security of existing and novel techniques • Studying solutions for proper integration with existing database management systems • Implementation of proof of concepts for performance evaluation Supervisor: Luca Ferretti
d	Multi-level organization of complex systems
	Keywords: Information theory, evolutionary computation, nonlinear dynamics, criticality, adaptation   Research objectives: The detection of intermediate structures in complex systems is not always a trivial task, while in contrast their characterization can lead to a meaningful description of the overall properties of the system, and in this way to its understanding. A large part of these structures is characterized by groups of variables (genes, chemical species, individuals, agents, robots…) that appear to be well coordinated among themselves and have a relatively weaker interaction with the remainder of the system. This general observation is the basis of several algorithms aiming to identify intermediate level structures in different fields: notable examples are the identification of functional neuronal regions in the brain, autocatalytic systems in chemistry, or the identification of communities in socio-economic systems. We are therefore interested in the identification and study of such structures, proposing algorithms and/or analysing data coming from the world of biology and of artificial systems. Supervisor: Prof. Marco Villani
e	Evolving artificial systems
	Keywords: Information theory, evolutionary computation, nonlinear dynamics, criticality, adaptation   Research objectives: Finding general properties of evolving systems has proven extremely difficult. A particularly interesting proposal is that evolution (either natural or artificial) drives complex systems towards “dynamically critical” states, which may have relevant advantages with respect to systems whose dynamics is ordered or disordered. According to this hypothesis, these critical systems can provide an optimal balance between stability and responsiveness. In this thesis we aim at determining under which conditions this turns out to be the case, by using abstract models. The systems described by these models will interact in an abstract “environment”, and the conditions under which critical systems have an edge will be analyzed. To achieve this ambitious goal, we will exploit the synergy among dynamical systems methods, information theory and evolutionary computation. Supervisor: Prof. Marco Villani
f	Multi-user activity recognition
	Keywords: activity recognition, inertial sensor, machine learning Research objectives: Human Actitivy Recognition (HAR) is a set of techniques which identifies the activity a user is performing, through the analysis of a set of sensors which describe actions and movements from users. This allows to configure a computing system based on the current scenario of the user, and provide a more tailored experience. More recently classic HAR system have also been proposed to detect and classify group actions, in which it is not only important to classify signals obtained from a single person, but also to share data among a set of devices carried by different people, which is the main topic of this thesis work. Proposed research activity: • State of the art on HAR for groups of people • Design and development of a data gathering platform • Analysis and study of techniques to realize HAR for groups of people • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: University of Stuttgart (Germany), University of Bamberg (Germany), University of New South Wales (Australia) Supervisor: Prof. Luca Bedogni
g	Real-time Collaborative 3D Editing
	Keywords: Research objectives: Collaborative editing ala Google Docs is still not widespread in the 3D world. The goal of this thesis is to explore real-time collaborative models for 3D. We obtain encouraging first results by extending distributed version control, ala git and GitHub, to 3D content. In this thesis, we plan to explore the design space of real-time collaborative 3D editing, focusing on local-first models such as CRDTs and differential synchronization. The thesis work be fully focused on developing new algorithm and data structure for graphics, disregarding all other aspects of real-world collaborative systems, such as security, networking, authentication, etc. No prior knowledge of distributed systems is required. Our prior work on version control for 3D includes: • MeshGit • MeshHisto • cSculpt • SceneGit • others under review Supervisor: Prof. Fabio Pellacini
h	Appearance Design on Surfaces
	Keywords: Research objectives: The realistic look of 3d surfaces is obtained by applying 2D images of patterns and textures to decorate the surface. But the application of a 2D image to a 3D surface incurs in distortions and seams. The goal of this thesis is to explore new algorithms for the design of patterns directly onto 3D surfaces. Our first results in this area were to port the most basic 2d operations onto surfaces. The focus of this thesis is to explore new algorithms for pattern designs on surfaces, including procedural generation, interactive design, and optimization. No prior knowledge of geometry processing is required. Our prior work on surface patterns includes: • b/Surf • GeoTangle • BoolSurf • others under review Supervisor: Prof. Fabio Pellacini
i	Domain Specific Languages for Appearance Design
	Keywords: Research objectives: Procedural appearance models use programs for the creation of realistic shapes and materials. In graphics, artists writes programs by wiring together nodes in a graph, that can be though of as a Domain Specific Language for appearance. This is such a common practice that all 3D software has its own procedural graphs system. But the basic ideas behind procedural graphs are now dated and do not scale to the complexity of modern appearance models. The goal of this thesis is to explore new features and possibly new domain specific languages to bring procedural graphs in line with current best practice in both appearance design and programming language design. Ideas to explore will be version control, refactoring, autocompletion, and copilot-like experience for appearance design. No prior knowledge of programming languages is required. Our prior work on procedural appearance includes: • pOp • others under review Supervisor: Prof. Fabio Pellacini
j	Controllable Procedural and Generative Appearance Models
	Keywords: Research objectives: The textures, patterns, and lights that are used to define the realistic appearance os surfaces are either measured, hand-painted, or synthesized by programs. The latter category, sometimes called procedural models, is the most scalable in the production of large amounts of content. But procedural models are hard to control. The goal of this thesis is to explore new algorithms for controlling procedural appearance models using recent results in machine learning. In particular, we will explore how to control generative models, like Dally and Stable Diffusion, by imposing constraints on the generated content. We will consider how to impose constraints on parametric models, such as procedural programs, by using automatic differentiation. Our prior work on controllable procedural appearance with ML includes: • pOp • others under review We have considerable prior work on controllable appearance with traditional methods such as: • AppWand • AppProp • SubEdit • AppIm • EnvyLight Supervisor: Prof. Fabio Pellacini
k	A modern and innovative learning system to enable efficient, lean and cost-effective public administration
	Keywords: digital twins, recommendation systems, video lectures, learning experience Research Objectives: Topics are digital transition, big data, accessibility and inclusiveness within educational systems. In particular, the research program focuses on the development of personalized learning models able to achieve two sustainability objectives specified by the United Nations General Assembly. These are SDG4 (Quality education — Ensure inclusive and equitable quality education and promote university learning opportunities for all) and SGD 10 (Reduced inequality — Reduce inequalities among on-site and on-line students and limit digital barriers to students with disabilities). Supervisor: Prof. Marco Furini Co-supervisor: Prof. Manuela Montangero
l	Creating a Bayesian phylogenetic algorithm for evolutionarily modeling of natural language syntax
	Keywords: quantitative phylogenetics, Bayesian analysis, syntactic parameters, natural language, reconstruction Research objectives: The goal of this project is to work out dedicated algorithms to extract historical signals from abstract natural language data (syntactic parameters), to reconstruct ancestral states, and to explain language transmission and diversification across time. The novelty of the project consists in the implementation of computational quantitative tools of Bayesian derivation on a type of language abstract structures (syntactic parameters) characterized by formal properties (for example, their multilayered deductive structure, www.parametricomparison.unimore.it) and peculiarities which are not observed in the linguistic data traditionally used in the field to automatically generate hypotheses of historical relatedness across languages: these peculiarities partially undermine the robustness of automatically-generated phylogenetic hypotheses because the quantitative tools which are traditionally used in linguistics are not able to deal with the peculiarities of syntactic characters and, consequently, to fully process the information provided by parametric data. To this end, novel/refined quantitative tools are required: this process is precisely aimed at implementing such tools. Possible connections with research groups, companies, universities: Research on this project will be conducted in collaboration with the Center for Language history and diversity, of which the Dipartimento di Comunicazione ed Economia is a member, and the Department of Linguistics at York, with whom the DCE has an academic cooperation agreement. The student who will be conducting this research will be asked to work in strict collaboration with the University of York. Supervisor: Prof. Cristina Guardiano Co-Supervisor: Prof. Giuseppe Longobardi, Dr. Dimitar Lubomirov Kazakov
m	Modeling structural interdependencies and their effects in constraining language diversity
	Keywords: networks, binary strings, implicational structure, machine learning, language transmission and change Research objectives: The goal of this project is to explore the multi-layered deductive structure of natural language grammars and its effects in constraining language diversity. In formal cognitive approaches to language structure and diversity, crosslinguistic structural variation is assumed to be universally constrained by a finite set of abstract points of variation (syntactic parameters) whose reciprocal interactions generate the attested surface diversity across languages, which turns then out to be predictable once the subset of variable abstract structures and their reciprocal interactions is fully defined and formalized. The purpose of this project is to explore and identify different possible types of parameter interdependencies and their effects on language acquisition and change, through the implementation of machine learning techniques and data-driven approaches able to disclose parameter dependencies which cannot be identified through manual data analysis, to formalize implicational rules among parameters, and to compare them with those produced by linguists’ expert judgement. These procedures will be aimed at: (a) discovering parameters that can be entirely dispensable as fully deducible from combinations of the others; (b) detecting further possible partial dependencies among parameters; (c) identifying the largest set of parameter values which are identically set in all the languages of a given family; (d) finding the least restrictive sets of parameter values that are sufficient to distinguish a family from all others. Possible connections with research groups, companies, universities: Research on this project will be conducted in collaboration with the Center for Language history and diversity, of which the Dipartimento di Comunicazione ed Economia is a member, and the Department of Linguistics at York, with whom the DCE has an academic cooperation agreement. The student who will be conducting this research will be asked to work in strict collaboration with the University of York. Supervisor: Prof. Cristina Guardiano Co-Supervisor: Prof. Giuseppe Longobardi, Dr. Dimitar Lubomirov Kazakov
n	Realizing parametric phylogenetic analyses of families or subfamilies of languages
	Keywords: language family, comparative methods, parametric comparison, computational phylogenies, data collection and analysis Research objectives: The goal of this project is to perform parametric comparison within historically established language families through the implementation of the formal and quantitative tools developed by the Parametric Comparison Method (www.parametricomparison.unimore.it), in order to explore their phylogenetic and historical structure and discover deeper historical relationships with other families. This investigation will be conducted using the comparison procedure designed and implemented by the PCM, which consists of the following toolkit: (1) a set of binary syntactic parameters which define crosslinguistic variation in nominal structures across the World’s languages: languages are represented as strings of binary values (+/-); (2) a set of implicational formulas defining parameter interdependencies: one value (though not the other) of a given parameter p1 may entail the irrelevance of another parameter p2, whose manifestations become then predictable; (3) a set of co-varying surface manifestations for each parameter; (4) a parameter setting procedure: each parameter is associated with a set of questions of the type “Does the (set of) structure(s)/interpretation(s) α occur in language L?”; only answers YES, which are based on positive evidence, set parameter values; (5) a set of distance-based and character-based quantitative algorithms and statistical procedures to extract historical signals from parameter values and generate language phylogenies. Possible connections with research groups, companies, universities: Research on this project will be conducted in collaboration with the Center for Language history and diversity, of which the Dipartimento di Comunicazione ed Economia is a member, and the Department of Linguistics at York, with whom the DCE has an academic cooperation agreement. The student who will be conducting this research will be asked to work in strict collaboration with the University of York. Supervisor: Prof. Cristina Guardiano Co-Supervisor: Prof. Giuseppe Longobardi, Dr. Dimitar Lubomirov Kazakov
o	Privacy in Mobile Crowdsensing Systems
	Keywords: crowdsensing, privacy, mobile computing, context-aware-computing Research objectives: Ubiquitous services gather personal data which is processed to provide tailored services to users. An example is Location Based Services (LBS), in which the location of the user is used to provide close point of interest, to track the movements and forecast future needs and alike. A specific use-case is that of Mobile CrowdSensing (MCS), in which a crowd of users collect data about a monitored phenomenon, and share it with a central entity. The data is usually geolocalized, to provide a more precise context of it. In this domain there are a number of potential issues which arise, specifically concerning the privacy of the users, which expose a lot of private information which, in case they are accessed by a malicious entity, may highlight private parts of the user routine. The proposed thesis will advance the understanding of privacy issues in MCS, by testing and analyzing available systems, while also designing state of the art algorithms which limit the information exposed, while still maintaining the service quality of the Ubiquitous application. Proposed research activity: • State of the art on MCS and on Privacy issues related to it • Test of real systems, with quantitative and qualitative analysis • Design and development of novel MCS data exchange protocols • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: IMDEA Networks (Madrid), University of New South Wales (Australia) Supervisor: Prof. Luca Bedogni
p	Efficient, scalable and flexible device to edge offloading
	Keywords: edge computing, machine learning, smart systems, context-aware-computing Research objectives: Edge computing refers to computing units which are placed at the edge of the network, close to the end devices. They typically serve as a lower latency computing unit to process information which devices cannot process on their own, due to battery or computational constraints. However the problem of defining how, when and what data has to be offloaded from a device to an Edge server is yet to be solved, as it encompasses a series of different parameters to take into account such as the operational characteristics of both the device and the edge server, the network, the time contraints on the results and so on. In this research these the PhD candidate will work towards a generalized model for Edge computing offloading from smart devices, which must take into account the heterogeneity of the data, the operational constraints of the device and of the edge server, the service quality to meet. Proposed research activity: • State of the art on Edge computing offloading • Design and implementation of a general purpose testbed for Device-to-edge offloading • Design, implementation and evaluation of Device-to-edge offloading protocols and techniques • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: University of California, Irvine (USA) Supervisor: Prof. Luca Bedogni
q	Automated Cyber Operations
	Keywords: cyber security, graph theory, planning, automation, artificial intelligence   Research objectives: The volume and complexity of modern attacks and defenses (as testified by the size of the CVE, CWE, CAPEC, MITRE ATTACK catalogues) is making manual exploitation and hardening of systems increasingly unfeasible. In the next few years we foresee an adoption of even more automated offensive and defensive tools and decision makers. This research thesis is motivated by the need for frameworks, algorithms, tools that help the human operator to carry on offensive operations (vulnerability assessments, penetration testing) and defensive operations (systems hardening, source code auditing) in a semi-automated fashion. The main goal is to explore the potential for automation and artificial intelligence in the activities involved in classical security assessments (representation of security-related knowledge, decision making, task planning and execution, creating digital twins, monitoring of progress, reporting) in order to improve efficiency and efficacy.   Supervisor: Mauro Andreolini
r	Privacy, Security and Resiliency of Authentication and Authorization
	Keywords: Cyber security, Applied cryptography, Authentication, Authorization, Distributed systems, Anonymity, Tracking   Research objectives: The emergence of novel computing systems is showing limitations of existing security solutions for authentication and authorization procedures, either due to limited capabilities of constrained devices and networks, limited usability and scalability within systems consisting of a huge amount of devices, attack surfaces including physical access to devices. Moreover, novel security paradigms require designing augmented security guarantees including increased privacy of users identities and reduced trust in identity providers. This research involves students in studying state-of-the-art authentication and authorization protocols, applied cryptography, and network security, acquiring expertise in analyzing and designing threat models and for novel secure computer systems. Proposed research activity: • State of the art on Web Authentication and Authorization, on privacy preserving protocols and distributed architectures • Analyzing and measuring trade-offs of privacy-preserving authentication • Analyzing deployment in real-world systems • Designing secure and practical authentication systems • Implementation of proof of concepts for heterogeneous platforms   Supervisor: Luca Ferretti
s	Scoring Systems for Cyber Security
	Keywords: cyber security, graph theory, algorithms, cyber ranges, monitoring   To increase the resilience of their infrastructures, both military and civilian organizations have started to train security personnel on cyber ranges, pre-arranged virtual environments through which it is possible to effectively simulate realistic security scenarios on a system architecture closely resembling the original one. Training goals are many and diverse in nature: to discover vulnerabilities in existing systems, to harden existing systems, to evaluate the security of a soon-to-be deployed component, to teach secure programming practices, to perform incident response on a compromised system. Unfortunately, current evaluation strategies  of student performance in an exercise share a severe limitation: they are focused exclusively on goal achievement (yes or no), and not on the specific path followed by the student. Therefore, giving a more precise assessment and, more generally, understanding the reasons behind success or failure, is impossible. This research is motivated by the need for techniques, algorithms and tools to model user activities in an exercise, compare the path carried out by the student with an “optimal” path devised by an instructor and suggest avenues for improvement. The goal is to propose novel cyber scores that can be used to capture the abilities of a student (speed, precision, ability to discover new vulnerabilities), highlight potential weaknesses, compare different students in the same scenario. Supervisor: Mauro Andreolini
t	Innovative and collaborative digital platforms to support effective computer science education
	Keywords: computer science education, collaborative platforms, data science, performance evaluation   Research objectives: The European job market is suffering an increased mismatch due to a severe shortage of workers with STEM and computer science skills. Several factors contribute to this issue, such as the need to train and support educators to include computer science in teaching activities and the low levels of self-efficacy of students towards this discipline. The main goal of this research is to design and develop a collaborative platform that supports educators in sharing projects, methodologies and experiences related to computer and data science education, including evaluation methodologies focused on continuous monitoring of students’ self-efficacy. The aim of the platform will be twofold: on one hand, giving educators the possibility to share their successful projects and innovative teaching approaches; on the other hand, allowing educators to find resources and communities that can be helpful to improve their experience.   Proposed research activity: ● Analysis on state of the art on educational collaborative platforms and tools ● Analysis of the literature about methodologies to improve and monitor students self-efficacy ● Definition of a framework for best practices sharing, collaborative creation of educational activities and continuous monitoring ● Design and development of a collaborative platform, fostering active collaboration with educators in computer science ● Eventual integration of computer science educational tools ● Test and validation of the developed platform ● Participation to relevant international schools and conferences   Supervisor: Prof. Claudia Canali
u	Edge computing systems to support modern IoT applications
	Keywords: edge computing, IoT applications, network simulators, microservices, performance evaluation   Research objectives: In the last few years Edge computing has emerged as a novel approach to support modern IoT applications based on microservices.These applications typically involve sensors located in multiple geographic locations producing big amounts of data. In such scenarios, traditional cloud approaches are not suitable due to latency constraints: an intermediate layer of edge nodes can host pre-processing and data aggregation tasks that can reduce the response time of latency sensitive operations. The main goal of this research is to design and develop scalable edge computing systems equipped with effective load balancing algorithms and adaptive management mechanisms. The research will involve the analysis of realistic smart mobility traces and the use of network simulators to design, develop, test and validate effective management solutions.    Proposed research activity: ● State of the art of edge computing systems for modern IoT applications ● Analysis of smart mobility traces ● Analysis of real modern IoT applications ● Design of load balancing algorithms for edge computing systems ● Development of simulation models for edge distributed systems  ● Test and validation of the proposed solutions platform ● Participation to relevant international schools and conferences   Supervisor: Prof. Claudia Canali
v	Latency Sensitive and Safety Critical GPU-accelerated real-time computing
	Keywords: GP-GPU, Massively parallel computing, Real-Time, Compute Architecture, Programming Models. Research Objectives: Nowadays cyber physical systems are characterized by data hungry algorithms within a wide variety of applications. This implies facing notable challenges for reaching the desired performance, hence the hardware deployed in domain such as Automotive, Robotics, Telecommunication and industrial automation are implemented as heterogeneous systems in which multi-core CPU hosts work in concert with massively parallel accelerators. In this context, a widely known accelerator is the Graphic Processing Unit (GPU), a hardware device designed to maximize compute throughput for general purpose computations (GP-GPU). It is not trivial, however, to exploit the full potential of the GPU processing power due to the notable architectural differences between GPUs and more traditional multi-core CPUs. Significant effort is therefore required, for instance, to exploit the recently released architectural features of modern GPUs, such as specialized cores for tensor processing and traversal of bounding volume hierarchies. Moreover, GPUs are designed to maximize throughput, hence inherently sacrificing latencies. This research aims at understanding how programming models, APIs and compilers could be enhanced in order to facilitate the work of the system engineer when implementing GPU accelerated applications, but also for accounting for stringent latency and safety requirements imposed by modern applications in the autonomous systems domain. Proposed research activity: ● State of the art on GP-GPU computing: from programming models to applications. ● Design and implementation of mechanisms that act at the level of APIs/programming models to enable real-time/safety critical GPU computing. ● Enhancing current compilers/source-to-source translators for simplyfing the programmer access to the GPU’s specilized cores (e.g. for tensor operations). ● Participation to relevant international schools and conferences. Supervisor: Nicola Capodieci Co-supervisor: Andrea Marongiu
w	Recommendation systems to personalize entertainment systems
	Keywords: Personalized entertainment, artificial intelligence, music/video sequencing, crossmedia promotion. Research objectives: The use of artificial intelligence (AI) to develop innovative and personalized multimedia experience. These models can personalize the music/video consumption process, help users to discover new sources, avoid the selection process, define strategies to engage users and multimedia material over social platforms, and more. Proposed research activity: • Design algorithms to understand the user’s habits in terms of multimedia material consumption • Design of AI-based algorithms to select material based on the user’s understanding obtained in step 1 • Design of recommender algorithms to personalize the multimedia experience • Design of strategies to engage users and multimedia material over social platforms • Design of sequencing strategies to meet the user’s habits and preferences in terms of multimedia material consumption Supervisor: Marco Furini Co-supervisor: Manuela Montangero
x	Time response analysis of scheduling policies on multiprocessors systems for real-time activities
	Keywords: Schedulability, Worst-case response times, real-time systems, multiprocessor systems Research Objectives:  This research activity aims to investigate the response time analysis of scheduling policies on multiprocessors for real-time systems. Real-time systems are characterized by stringent timing constraints where tasks must meet their deadlines to ensure correct and reliable operation. Multiprocessor platforms offer increased computational power and can handle multiple tasks concurrently, making them suitable for real-time applications. However, the scheduling policies employed in these systems significantly impact task response times and meeting the required deadlines. The research will focus on evaluating scheduling policies and their impact on response time in multiprocessor environments, i.e, evaluate and quantify the time it takes for a task or job to complete its execution in a system. In the context of real-time systems, response time analysis is crucial to assess the predictability and performance of scheduling policies in meeting the timing requirements, ensuring reliable and timely system operation. The objective of the research is to analytically derive bounds to the worst case delays caused by the scheduling policies when completing task jobs, with respect to their deadlines.    Supervisor: Prof. Manuela Montangero
y	Large Language Models for Knowlege Graph Completion
	Keywords: Knowledge graph, large language models, deep learning, natural language understanding   Research objectives: The most recent methods in the literature to perform automatic completion of corpus-based KGs exploit large language models, LLMs, deep learning models for language processing and generation. However, these techniques completely leave out the structure of the KG in the KGC by completely relying on the knowledge gained from LLMs in training and thus limiting the effectiveness of such solutions.   This PhD project is in the field of machine learning, natural language processing, and knowledge representation. Methods of KGC based on classical and modern approaches will be explored, with a focus on solutions that combine structural information from KG with knowledge implicitly contained in LLMs. New learning techniques that are able to extract and use contextual knowledge from LLMs for KGC will be developed.   Proposed research activity: ● Large language models ● Knowledge Graphs ● Techniques for Knowledge Graph Completion ● Deep learning   Supporting research projects (and Department): The PhD student will be hosted at the Department of Physics Informatics and Math and will be mainly supported by research projects in the clinical medicine field. The PhD student will have the opportunity to work on the related project Prof. Mandreoli is conducting in collaboration with ExpertAI.   Possible connections with research groups, companies, universities: ExpertAI, Modena, Italy.  Supervisor: Prof. Federica Mandreoli
z	Data Analytics and Machine Learning for P4 medicine
	Keywords: Big data, data analytics, machine learning, health data Research objectives: Medical practice is evolving rapidly, away from the traditional but inefficient detect-and-cure approach, and towards a Preventive, Predictive, Personalised and Participative (P4) vision. This vision is increasingly data-driven, and is underpinned by many forms of “Big Health Data”. The main research objective of this PhD project is to contribute to the above ambitious goals with big data analytics and machine learning solutions for collections that include a broad variety of (longitudinal) data including genetics, electronic health records, patient reported outcomes, lifestyle indicators and wearables. The project is in collaboration with the Unit of Infection Diseases of the University Hospital Policlinico di Modena and the School of Computing at Newcastle University (UK). The PhD candidate will have the opportunity to work with retrospective and prospective health data collections mainly in the context of infection diseases, also including COVID-19 data. Proposed research activity: ● Data cleaning techniques and application to clinical and self-generated datasets; ● Study of the state of the art of data mining and machine learning techniques in the field of medicine and people wellness; ● Study of Machine Learning solutions for P4 medicine; ● Model Interpretability and P4 medicine; ● ML model evaluation and assessment; ● Standardization of new data-driven clinical scores. Supporting research projects: The PhD student will be hosted at the Department of Physics Informatics and Math. Prof. Mandreoli has active projects with Prof. Guaraldi and Prof. Missier. The project will use retrospective Electronic Health Data, mainly from Policlinico of Modena, prospective health data generated by IRB approved studies. Possible connections with research groups, companies, universities: The study will be conducted together with Prof. Giovanni Guaraldi (Dipartimento Chirurgico, Medico, Odontoiatrico e di Scienze Morfologiche con Interesse Trapiantologico, Oncologico e di Medicina Rigenerativa), physician of the Infection Disease Unit of Policlinico of Modena, and his research group. The co-tutor Prof. Paolo Missier is a reader with the School of Computing at Newcastle University. Joint research activities are also currently active with the National Innovation Centre for Data (NICD -UK). Supervisor: Prof. Federica Mandreoli
aa	Enhancing Performance and Efficiency of Deep Learning Models for Human-machine Interaction Applications
	Keywords: neural machine translation, image captioning, language detection, performance, deep learning Research Objectives: Human-machine interaction domain is composed of important applications such as neural machine translation, image captioning, and language detection. However, these models often suffer from issues such as poor performance, high computational complexity, and limited scalability. The proposed research project aims to investigate and develop novel techniques to improve the performance of these models. The research project also aims to evaluate the effectiveness of these techniques on various benchmark datasets and compare them with existing state-of-the-art techniques. Proposed Research Activity: ● Literature review and state-of-the-art analysis of neural machine translation, image captioning, and language detection models ● Development of novel techniques to improve the performance of these models, such as attention mechanisms, transfer learning, and model compression ● Evaluation of the proposed techniques on various benchmark datasets, such as WMT, COCO, and LDC ● Comparison of the proposed techniques with existing state-of-the-art techniques ● Participation in relevant international conferences and workshops Supervisor: Roberto Cavicchioli Co-supervisor: Alessandro Capotondi
bb	Efficient HW/SW solutions for constrained intelligent autonomous systems
	Keywords: TinyML; Deep Learning; HW/SW co-design; embedded systems; Research Objectives: This project focuses on developing advanced embedded autonomous platforms for intelligent edge systems. The main objective is to enable devices with limited resources to run AI applications, including deep learning workloads and Spiking Neural Networks, targeting HW/SW co-design methodologies. These include software development methodologies tailored for embedded and IoT autonomous systems and Hardware Description Languages (HDL) or High-Level Synthesis (HLS) languages for the design of reconfigurable architectures. These methodologies will be integrated into commercially available and open-hardware devices, particularly those based on the RISC-V architecture. Prior works on the topic: ● CMIX-NN: https://ieeexplore.ieee.org/document/9049084 ● HERO https://dl.acm.org/doi/10.1145/3295816.3295821 ● PULP https://pulp-platform.org/ Supporting research projects (and Department): This project will be carried out at the Department of Physics, Informatics and Mathematics (FIM), and aligns with EU and regional initiatives such as MASA (https://www.automotivesmartarea.it/) and AI4CSM (https://ai4csm.eu/). Possible connections with research groups, companies, universities: The project will be carried out in collaboration with European academic institutions like SUPSI Lugano, ETH Zurich, KU Leuven, and extra-EU like Technology Innovation Institute di Abu Dhabi. Supervisor: Prof. Andrea Marongiu Co-supervisor: Alessandro Capotondi
cc	Compiler-aided parallel programming model for next generation high performance predictable heterogeneous platforms
	Keywords: Compilers; Parallel Programming Models; Runtime; Heterogenous Systems;   Research Objectives: The primary focus of this project is to address the programming challenges associated with emerging high-performance heterogeneous systems. The project aims to develop compiler and runtime support for heterogeneous and parallel programming models, explicitly targeting the cyber-physical systems domain (robotics, automation, manufacturing). The objective is to enhance the adoption of these systems by improving performance and timing predictability, while maintaining a simple programming interface. Proposed Research Activity: ● In-depth study of the challenges involved ● Design and development of compiler and runtime system extensions specifically tailored for Commercial-off-the-Shelf (COTS) platforms and open-source hardware architectures like RISC-V. ● Validate the proposed solutions on real-life problems from the targeted application domains ● Participation in relevant international conferences and workshops Prior works on the topic: ● HEPREM https://ieeexplore.ieee.org/document/9035630 ● HERO https://dl.acm.org/doi/10.1145/3295816.3295821 ● PULP https://pulp-platform.org/ Supporting research projects (and Department): This project will be carried out at the Department of Physics, Informatics and Mathematics (FIM), and aligns with the research activities of EU project AI4CSM (https://ai4csm.eu/). Possible connections with research groups, companies, universities: ● ETH Zurich, Switzerland ● University of Massachussets, Lowell, USA Supervisor: Prof. Andrea Marongiu Co-supervisor: Alessandro Capotondi
dd	Environmental information and communication: fake news, bias and distortion, and data analysis. Possible impacts on policy and public opinion.
	Informazione e comunicazione ambientale: fake news, distorsioni e analisi dei dati. Possibili impatti sulle politiche e sull’opinione pubblica. Keywords: Environmental information, Information and communication theory, social media and conversation analysis, Cognitive and perceptual biases and climate change, Qualitative and quantitative methods in data analysis.   Research objectives: The purpose of this research area is to analyze the processes of dissemination and production of fake news, cognitive bias related to information on the issues of environment, climate crisis and climate change. The intention is to work on textual corpora and conversations regarding environmental communication and information issues, both online and social media, and offline, by:  (a) acquiring skills regarding the relationship between quantitative and qualitative data analysis; and (b) using tools such as NVivo and Altas and qualitative methodologies related to thematic, narrative analysis and different forms of “netnography” of data.   Supervisor: Prof. Federico Montanari, DCE

Scholarships with specific topic

Scholarships funded by Regione Emilia Romagna

5

Smart mobility and planning personalizzato dagli utenti con Open Data e Crowdsensing

Optimized smart mobility planner leveraging Open Data and Crowdsensing Regione Emilia Romagna Research Objectives: This thesis studies the possibility to leverage open data and crowdsensing to optimize and improve users movements within a city. Modern smart cities provide a plethora of sensors and data which however it is not fully utilized to their maximum potential, leaving exciting possibilities unexplored. This thesis focuses on this challenge by integrating open data and crowdsensing into a smart mobility planning framework. Open data sources provide several information, including real-time traffic conditions, public transportation schedules, environmental parameters and alike, and can be utilized to improve the liveability of smart cities by users. Moreover crowdsensing techniques allow to collect real-time data from heterogeneous sets of sources, such as smartphones and wearable devices. Proposed research activity: ● State of the art relevant to the research ● Data analysis ● Framework design and development ● Test of the framework Supervisor: Prof. Luca Bedogni

Borse PNRR

CN4 Spoke 6

6

Novel Driver Monitoring Systems (DMS) for driver empowerment in partially automated vehicles

Keywords: driver monitoring system, partially autonomous vehicle, Advanced Driver Assistance Systems , Driver Monitoring Systems, driver empowerment Research objectives: The research activity will focus on Driver Monitoring Systems (DMS) designed to capture and process physiological signals to continuously detect and monitor the state of the driver in partially automated vehicles, to empower the role of the driver instead of replacing it. The candidate will benchmark existing DMS, as well as Advanced Driver Assistance systems (ADAS) designed to achieve an immersive driving experience and ensure safe high driving performance. The candidate will also investigate how to implement a novel DMS concept, relying on multiple non-intrusive sensors and Artificial Intelligence (AI) algorithms, and will identify the computational requirements to select the most suitable high-performance real-time systems for innovative human-centric Interaction paradigms in partially autonomous vehicles. Supporting research projects PNRR CUP E93C22001070001, Spoke 6 Supervisor: Prof. Marko Bertogna

PNRR – DM 117
PNRR, Missione 4 “Istruzione e ricerca”,
Componente 2 “Dalla Ricerca all’Impresa”
Investimento 3.3 “Introduzione di dottorati innovativi che rispondono ai fabbisogni di innovazione delle imprese e promuovono l’assunzione dei ricercatori dalle imprese”

7	Software di Piattaforma Sicuro ed Affidabile per Computer Integrati ad Alte Prestazioni per Sistemi Autonomi e Definiti dal Software
	Safe and Secure Platform Software for High-Performance Embedded Computers Targeting Software-Defined and Autonomous Systems Keywords:   Research objectives: Complex high-performance embedded systems are rapidly being adopted, also within applicative domains where safety and security requirements are strict. They include autonomous control and driving for automotive, railway and avionics, as well as smart robots and automations. Traditionally, these contexts have been powered by simple and predictable embedded systems that now have insufficient against the growing computing needs. New platforms, instead, enable artificial intelligence computing, e.g. by employing multicore CPUs, programmable GPUs, FPGAs, and/or neural accelerators. While the hardware platform evolves, the software stack is introducing elements providing high flexibility, like virtualisation and hypervisors, as well as isolation in the execution, like compartmentalisation and containerisation. They are useful to enable cloud-native development and integration process, which provide continuity between edge and cloud. They are also useful to enable deep and fast functional reconfiguration of the platform, so to realise software-defined vehicles. Lastly, they are needed to guarantee predictable performances to support safe and secure applications. The goal of the research is to design methodologies and technologies that improve the state of the art of system software addressing one or more of these challenging trends. Possible relevant topics include, but are not limited to the following: ● Containers execution environments for highly-predictable computing time and/or for accelerators support. ● High-predictability for compute offloading to accelerators like GPGPU, FPGA o ASIC for deep-learning inference. ● Partitioning and configuration systems for (dynamic) assignment of memory subsystem resources like caches, coherent interconnects/meshes, DRAM controllers. ● Improve and simplify quality of hardware and software configuration systems. ● Enable the integration of quality-of-service policies into high-bandwidth communication protocols between heterogeneous components in the same system-on-chip and/or between systems in the same distributed embedded environment. Supporting research projects MUR – DM 117   Possible connections with research groups, companies, universities. Minerva Systems Srl (co-funder) – The successful candidate will spend 6 months with the company. Technical University of Munich (Germany) Boston University (USA) University of Waterloo (Canada) Supervisor: Prof. Andrea Marongiu Co-supervisor: Dr. Marco Solieri (Minerva Systems Srl)
8	Controllo del Movimento dei Veicoli per Sistemi di Guida Autonoma
	Vehicle Motion Control for Autonomous Driving Systems Keywords: Real-time embedded systems, Autonomous Driving, Vehicle Dynamics, Control Systems   Research objectives: The research activity will focus on vehicle motion control design to manage the longitudinal, lateral and vertical dynamics of modern vehicles.A new approach for integrated vehicle motion control will be investigated to scale a variety of different vehicles configurations without redesign and coordinating multiple vehicle subsystems as friction brake system, wheel drive electric motors, wheel steer actuators, camber angle actuators, suspensions actuators and actuators generating additional normal forces. As part of the research, a focus on autonomous vehicles will address energy-efficient embedded platform as on-board computers for trajectory planning and tracking, in the domain of vehicle platooning and V2V-enabled autonomy. A structured tunable vehicle motion control can enable the customization of vehicles by providing different motion feelings and generating adaptive trust feelings to passengers in case of autonomous driving.The candidate will explore the possibility of accelerating both “classic” geometrical, and model based, and more modern AI-based , also adopting advanced Driver In The Loop simulators and HIL Test Benches.   Supporting research projects MUR – DM 117   Possible connections with research groups, companies, universities. Danisi Engineering Srl (co-funder). The successful candidate will spend 6 months with the company. Supervisor: Prof. Marko Bertogna Co-supervisor: Dr. Luigi Pazienza
9	Sistemi di Sterzo dei Veicoli in Tempo Reale
	Real-Time Vehicle Steering System Keywords: Real-time embedded systems, Vehicle Dynamics, Control Systems   Research objectives: The research activity will focus on enhancing steering systems for software-defined vehicles, guaranteeing quick response, low power consumption and excellent stability. As part of the research, different active steering high-level control strategies will be analyzed and tested in a virtual environment, in compliance with co-simulation and real-time constraints. Algorithms will be developed, taking into account the real-time control of electric actuation and high-level ADAS features. The candidate will explore the trade-off between classic model-based and new AI-based controllers, evaluating them in terms of performance, energy consumption, correlation with experimental testbeds.   Supporting research projects MUR – DM 117   Possible connections with research groups, companies, universities. Danisi Engineering Srl (co-funder). The successful candidate will spend 6 months with the company. Supervisor: Prof. Marko Bertogna Co-supervisor: Dr. Luigi Pazienza

PNRR – DM 118
Missione 4, Componente 1“Potenziamento dell’offerta dei servizi di istruzione: dagli asili nido all’Università”
Investimento 3.4 “Didattica e competenze universitarie avanzate”
Investimento 4.1 “Estensione del numero di dottorati di ricerca e dottorati innovativi per la pubblica amministrazione e il patrimonio culturale”

10	Dataset Per La City Science: Sviluppare Strumenti Di Data Analysis Per Migliorare Le Politiche Pubbliche Urbane
	Dataset For The City Science: Designing Data Analysis Tools To Improve Urban Public Policies Hosting public administration:  Comune di Reggio Emilia – The successful candidate will spend at least 6 months in the hosting public administration. Keywords: database, data analysis, deep learning, city science, policy innovation, public management Research Objectives: Cities are at the core of the most societal challenges in Europe, which need new frameworks and partnerships to better respond to their features. For example, the City Science approach is devoted at strengthening the ways in which research and science can be used to improve urban policies and policymaking processes. In this direction, the amount of data both analogical and digital regularly produced by local municipalities constitutes a potential fruitful repository to analyze and innovate responses to urban challenges and provide indicators for policy making. Thus, the research project focuses on the building, development, and analysis of digital databases related to specific policies that impact on urban dynamics (for example, in the field of welfare, environmental sustainability, mobility, etc.). Also, one of the main objectives is to provide data analysis tools and deep learning models able to guide the re-elaboration of the same policies, in line with the Regulation on Democracy, Urban and Climate Justice developed by the Municipality of Reggio Emilia. Supporting research projects MUR – DM 118 Supervisor: Prof. Damiano Razzoli Co-supervisor: Roberto Cavicchioli
11	Sviluppare, Gestire E Mantenere Una Piattaforma Digitale Pubblica Per Favorire Una Prossimità Urbana Ibrida
	Developing, Managing, And Mantaining A Digital Municipal Platform To Foster Urban Hybrid Proximity Hosting public administration:  Comune di Reggio Emilia – The successful candidate will spend at least 6 months in the hosting public administration. Keywords: hybrid proximity, urban platform, community welfare, code/space, socio-technical context, social sustainability, city science Research objectives: The research project focuses on the analysis and implementation of a public digital platform (platform municipalism), with a specific focus on the ability of digital platforms to rebuild and maintain proximity relationships and facilitate the strengthening of the social fabric. In particular, the project aims at elaborating and applying an approach to digital spaces as public commons, in order to hold together the physical and digital levels. At the same time, we intend to verify the needs, skills, actions of the figure of welfare community manager applied to neighborhoods as emerging sociotechnical contexts. The aim is to evaluate the characteristics and contents of a digital city model, as opposed to the image of the smart city, and of a possible statute of digital citizenship to facilitate hybrid proximity dynamics. The concept of platform municipalism can be viewed as a potential solution to provide for neighbors a public space to develop digital e-commerce spaces, to respond to welfare needs through digital sharing spaces, to be updated on what is going on within the community, to design sand analyze data produced by the community for the needs of the community itself (ex. rate of separate waste collection, availability and engagement of local volunteers, etc.)   Supporting research projects MUR – DM 118 Supervisor: Prof. Damiano Razzoli
12	Monitorare L’industria Meccatronica A Reggio Emilia: Database, Dashboard E Analisi Dei Dati Per Definire Politiche Pubbliche A Supporto Della Crescita Sostenibile Della Supply Chain
	Monitoring The Mechatronic Industry In Reggio Emilia: Database, Dashboards, And Data Analysis For Public Policy Design To Support The Sustainable Growth Of Its Supply Chain Hosting public administration:  Comune di Reggio Emilia – The successful candidate will spend at least 6 months in the hosting public administration. Keywords: mechatronic, supply chain, sustainability, city science, database, data analysis, circular economy, public management Research Objectives: The project is aimed at mapping the production chain of the mechatronic industry in the province of Reggio Emilia, whose role is strategic in the economy of the territory. It aspires to the timely profiling of the industrial network and its monitoring, through the construction and systematic updating of a database and a dashboard that allow to identify public policies to support the sustainable growth of the supply chain, with particular attention to the opportunities offered by new business models that can be placed in circular economy projects.   Supporting research projects MUR – DM 118 Supervisor: Prof. Matteo Rinaldini
13	Algoritmi e Sistemi Intelligenti Efficienti per Applicazioni Edge Computing in ambito Smart-City
	Efficient Intelligent Algorithms and Systems for Smart-City Edge Computing Applications Hosting public administration:  Comune di Modena – MASA – The successful candidate will spend at least 6 months in the hosting public administration. Keywords: neural networks, embedded systems, optimization, memory constraints, smart-city, CCAM Research Objectives: In the context of Smart-City applications, edge computing has emerged as a key paradigm that enables real-time processing, low latency, and reduced bandwidth requirements. This is crucial to enable Connected and Automated Mobility (CCAM) services and next-generation Advanced Driver Assistance Systems (ADAS) to rely on the correctness of Smart-City data. However, deploying state-of-the-art (SoTA) AI models on resource-constrained edge devices presents significant challenges. This research proposal aims to investigate efficient, intelligent algorithms and systems for edge computing applications in Smart-Cities, focusing on techniques for efficient deployment of SoTA models, novel models tailored for edge computing, and novel hardware accelerators for AI. The Modena Automotive Smart Area, an open lab in the Modena area with network and computation capabilities, will serve as the testing platform for the designed research products for testing and deploying CCAM applications. Proposed Research Activities: ● Literature Review: Conduct an extensive review of existing research on AI application edge computing and AI model optimization for edge devices, with a specific focus on Smart-City applications. Identify the current challenges and gaps in the field and explore the latest advancements and emerging trends. ● Develop novel techniques for efficiently deploying SoTA AI models on edge devices for Smart-City applications, while ensuring low latency, reduced energy consumption, and optimal resource utilization. ● Design and develop novel AI models tailored for edge computing in Smart-City applications, taking into account the specific requirements and constraints of the environment. ● Evaluate the efficiency and effectiveness of the proposed techniques and models through benchmarks and real-world Smart-City edge computing scenarios. ● Compare the proposed techniques and models with existing state-of-the-art techniques and models. ● Collaborate with ETH-Zurich and NVIDIA Corporation to leverage their expertise in hardware and software optimization for embedded platforms and Smart-City applications. ● Participate in relevant international conferences and workshops to disseminate the research findings and exchange knowledge with other experts in the field. Supporting research projects MUR – DM 118 H2020 – dAIEDGE (FIM) Possible Connections with Research Groups, Companies, and Universities: ETH-Zurich, NVIDIA Corporation Supervisor: Roberto Cavicchioli Co-supervisor: Alessandro Capotondi
14	Profili Giuridici della Trasformazione Digitale delle Infrastrutture e della Sperimentazione su Strada di Soluzioni di Guida Autonoma
	Legal Profiles of the Digital Transformation of Infrastructures and Road Testing of Autonomous Driving Solutions Hosting public administration:  Comune di Modena – MASA Keywords: Digital Transformation, Infrastructure, Autonomous Driving, Legal Aspects, Road Testing Research Objectives: The research aims at analysing the legal profiles of autonomous driving, with a focus on aspects related to the digitalisation of infrastructures, the regulation of the testing phase on public roads, the compliance with data protection regulations, the implementation of autonomous driving in road traffic and the resulting ethical and liability profiles. The investigation will be extended to the impact of the AI Act, currently in phase of approval at the EU level, on the development of autonomous driving solutions. The successful candidate will spend at least 6 months in the hosting public administration. Supporting research projects MUR – DM 118 Supervisor: Prof. Marko Bertogna
15	Transizione digitale per Comune Smart
	Digital Transition for a Smart Municipality Hosting public administration:  Comune di Mantova Research Objectives: As part of the activities related to the development of the projects submitted by the Municipality of Mantova through the announcement of Mission 1 – “Transizione Digitale del PNRR”, the candidate will have the task of analyzing the actions implemented by the Municipality of Mantova through its In-House Company – ASTER SRL. The candidate will thus identify innovative solutions for the integration of the application platforms in use in the Institution with those being adopted, in order to comply with the paradigms of Single Sign On, Once ONLY, usability and accessibility, big data and analytics. The candidate will also develop analyses and implementations to allow the integration of the systems in use in the Institution with the National platforms, and in particular with PND and PDND, as well as to design a possible scenario for the development of SMART CITY projects applied to urban mobility. The successful candidate will spend at least 6 months in the hosting public administration. Supervisor: Prof. Marko Bertogna

PNRR – DM 118
Missione 4, Componente 1“Potenziamento dell’offerta dei servizi di istruzione: dagli asili nido all’Università”
Investimento 3.4 “Didattica e competenze universitarie avanzate”
sotto-misura T1 “Assegnazione di nuovi dottorati triennali in programmi dedicati alle transizioni digitali e ambientali”

16

Nuove Sfide nell’Era della Transizione Digitale

Novel challenges in the Digital Transition Era Research Objectives: To ensure the success of the National Recovery and Resilience Plan‘s (PNRR) initiatives for a digital and ecological transition, it is crucial to develop innovative technologies and design methodologies for future digital services. These services will focus on collaboration, distribution, and dynamism, harnessing the power of Artificial Intelligence , Distributed Systems and Big Data and exploring advanced techniques and strategies to safeguard digital systems and protect them against cyber threats. The objectives are to enhance productivity, facilitate seamless communication between humans and machines, and prioritize privacy and security considerations. Possible connections with research groups, companies, universities. Minerva Systems Srl – The successful candidate will spend 6 months with the company. Supervisor: To be defined (refer to the Coordinator for questions related to the scholarship)

Previous Cycles

Research Theses of the XXXVIII Cycle