Admissions

The Call for Admission to the XXXIX cycle of the three-year PhD Programme starting in October 2023 can be downloaded here. The deadline is July 20th 2023 at 12:00 p.m. CET (noon).

The positions available for the PhD programme in Computer Science and Mathematics and the admission procedure are described in the Annex 13 in the Call for Admission.

Total number of positions with fellowships n.19:

•                D.M. 118/23 PNRR 4.1
•               Title: Tech4Math-Math4STEM 
•               Description: The aim of the research is to identify guidelines for the training of mathematics teachers in innovative technologies and methodologies that promote the learning of mathematics and STEM disciplines and the development of soft, trans-disciplinary, skills in 21st century citizens. Mixed methods, both qualitative and quantitative, typical of educational research in the scientific disciplines will be used. The research will have an initial direct impact on the Apulian territory through experiments on STEM subjects that will involve schools in the area and will pay particular attention to promoting the development of scientific skills in female students. 

•               D.M. 118/23 PNRR 4.1
•               Title: Nonlocal differential problems in Quantum Science
•               Description: The research will be mainly dedicated to the study of nonlinear equations with nonlocal interactions (Pekar-Choquard  equations, Hartree type equations, equations and systems of nonlinear dispersive equations, fractional  Schrodinger equations). These problems find applications in Quantum Mechanics, in dislocation  problems, of phase transition, in image reconstruction. One of the objectives will be to address the study of some classes of interaction functionals, where classical PDE theory fails due to of the presence of non-locality, such as density functionals of the Thomas–Fermi–Dirac–von type Weizsäcker (TFDW), resulting from the description of the response of a  layer of graphene.

•               D.M. 118/23 4.1 Patrimoni Culturali 
•              Title: Analysis and valorization of digitized artistic heritage using Artificial Intelligence techniques  
•               Description: Research topics will cover the development of Artificial Intelligence models for the analysis and enhancement of digitized artistic heritage. The project has an impact on the territory, as artistic heritage plays a role of strategic importance for the country's economic and cultural growth. In fact, the expected results, to the extent of wider enjoyment of artistic heritage, would have positive repercussions on the cultural sector, which has been severely affected by the recent pandemic crisis. In addition, the research responds to the emerging demand for new professional figures whose skills lie at the intersection of the humanities and computer

•               D.M.117/23
•               Title: Topological Data Analysis and optimization for industrial processes
•               Description: The research proposal will focus on developing computational models that integrate Topological Data Analysis-TDA concepts with machine learning and optimization techniques to better understand the phenomena that the data under analysis represent. The project idea aims to design variants of supervised learning and dimensionality reduction models that through appropriate optimization methodologies integrate the topological representations of the data into the approximation model to enable a better understanding of their internal data structures and the underlying properties that characterize the real domain of which the data are a representation. 

•               D.M.117/23
•               Title: Artificial Intelligence Approaches for Digital Healthcare through Pose Detection and Recommender Systems in eHealth 
•               Description: Digital technologies in healthcare can improve efficiency, reduce costs, increase patient engagement, and lead to better health outcomes. This doctoral project will investigate the possibility of monitoring biological parameters, pose, and activities using wearable and/or IoT technologies. This will allow the physician to obtain a complete health picture of the patient and intervene in case of emergency. Thus, the data collected will also enable feeding recommendation systems for active aging and well-being activities. Providing, for example, personalized activities, targeted food plans, and physical exercises to be performed. The candidate is therefore expected to be able to provide innovative, impactful solutions in the field of eHealth and Artificial Intelligence.

•               D.M.117/23
•               Title: Study of AI techniques for efficient generation of digital humans and 3D environments
•                Description: The research will focus on the study and development of generative AI models for creating Digital Human and modeling 3D environments. The project goal is to develop new research skills in the field of Artificial Intelligence, with a focus on the emerging field of Generative AI. The project idea aims to use generative AI techniques for both text and image generation and the creation of "Digital Human" avatars that are able to replicate human behavior in terms of features, gestures, expressions, and voice. This field of research is innovative and promising and aims to develop algorithms and models that can autonomously generate new content, such as images, videos or text. In addition, Generative AI offers opportunities for innovation and development in various fields, such as art, design, medicine, and many others. 

•               D.M.117/23
•               Title: Innovative tools and processes to support predictive analysis in the e-commerce and fintech fields.
•               Description: Analysis and formalization of artificial intelligence and machine learning tools and methodologies in the fintech field to analyze macroeconomic data and market indexes to predict future trends in the sector. Development of personalized recommendation systems to improve users' online shopping experience: development of intelligent systems that analyze purchase data and user preferences to improve the online shopping experience, helping users discover relevant products and increasing sales for retailers.

•                D.M.117/23
•               Title: Pure augmented reality for remote assistance through mobile and smartglass video calls on web apps and apps 
•               Description: The aim of the research is the proposition and experimentation of methods that allow the user to insert objects in Augmented Reality during a remote assistance video call or even during a simple view through a camera The objects must be fixed in space, being able to be referenced with respect to the inertial platform of the host device, the man-machine interaction must take place via the web and via the app. 

•               D.M.117/23
•               Title: Explainable Artificial Intelligence (XAI) on Time Series Data
•               Description: The object of the research is essentially Intelligent Data Analysis. Time series forecasting is fundamental for various use cases in different domains and in particular in the financial one. The typical design process includes five sections that are commonly organized into a pipeline structure: (1) data preprocessing; (2) feature engineering; (3) hyper-parameter optimization; (4) method selection ;(5) assembly;  The research intends to analyze and compare the automation methods identified for each section of the pipeline. Furthermore, it is necessary to investigate how much the automation of the pipeline can be of help for the "eXplainable AI" which is one of the much studied and of great interest aspects for the usability of the Intelligent Data Analysis in business domains.

•               D.M.117/23
•               Title: Human-centric Cybersecurity 
•               Description: The "national security-centric" approach dominates cybersecurity policies and practices. In the context of the "Competing Visions for Cyberspace", the research activity aims to propose an alternative approach to cybersecurity that derives from the traditional "human security".  Rather than prioritizing technological vulnerabilities, this approach prioritizes them  to human behavior by identifying the networks  as an essential part of modern cyber  threat intelligence, analyzing the human cyber reaction in the face of an OT threat, network disservice, ransomware or interruption of corporate business continuity. A humancentric approach is committed to outlining risk insights based on human monitoring by defining what are the weaknesses dictated by the analysis of human misbehaviour. 

•               Title: Enhanced User Experience in no-code software application
•               Description: According to many analysts, software development in the future will be no-code/low-code, i.e. it will also be feasible by operators who do not have advanced IT skills, the so-called Citizen Developers. The project intends to explore and formalize the possible methodological, technical and functional solutions that can be adopted to improve the user experience of Citizen Developers in the development of complex application solutions. The ultimate goal is to drastically reduce the skills gap required for the adoption of a no-code application development platform. The project will be carried out in collaboration with the proposing company not only in the solution identification phase but also in the implementation of the was about to evaluate its benefits with the community of corporate stakeholders. 

•               Project FAIR S6.  SYMBIOTIC AI 
•               PNRR PE CUP: H97G22000210007,  codice progetto: PE00000013, S6  SYMBIOTIC AI
•               Title: Symbiotic AI
•              Description: The research of the 6 projects will be focused on the following objectives and will  be supervised in specific  Work Packages (WP):

1. Study and application of methods and guidelines for human-centered design of symbiotic systems,  WP6.1: Design of SAI systems
2. Understanding human needs in Symbiotic AI: towards Symbiotic Recommender Systems, WP6.2: Human understanding capabilities of SAI systems
3. Semantically enriched neural-symbolic methods for Knowledge Graph Refinement and Explanation, WP 6.3: User input for SAI systems performance
4. Explainability of Artificial Intelligence systems for medical applications., WP6.4: Understandability of SAI systems
5. Machine learning model recycling for biomedical applications, WP6.6.2: Design of new AI methods for model recycling
6. Data-Centric AI approaches in Symbiotic AI,  WP6.6.3: Design of new AI methods for transforming raw data into smart data

Total number of positions with fellowships n.19.