Reserved topic scholarships
The Doctorate Program in Industrial Innovation offers mainly 2 types of 3-year PhD positions:
- Standard PhD positions with scholarships financed by the companies;
- PhD Executive positions, which are without scholarships and reserved to the partner companies' employees who maintain their status (and salary) within their company. The applicants can be either already employed by the company, or they can be employed after the selection process, but before the PhD Program starts. The financial conditions are defined directly between the company and the applicant.
40th Cycle - Intake year 2024
Leonardo S.p.A. - 2 scholarships
A - Designing scalable and efficient data-centric and traditional HPC applications on emerging parallel architectures
The research activity aims to study and evaluate innovative technologies in the context of data-centric applications (e.g. Artificial Intelligence workflows), in particular hardware with processing in memory capabilities, in-network computing on smartNIC and RISC-V processors, together with their programming methods and associated optimizations.
The activities will involve a first period of study (training courses), after which the actual research activities will start, supplemented with a period to be spent at the Company and a period to be spent abroad. After a first phase dedicated to approaching the new technologies of interest, there will be an evaluation of possible industrial use cases and the development of algorithms and ad-hoc techniques in order to use such hardware at best, in such a way to be able to implement such solutions in the company’s products.
The expected results will correspond to an increased know-how on these technologies and their practical implementation on industrial use cases or prototype examples, to be agreed on with the industrial partner during the course of the project.
For what the training activities are concerned, during the first months of the PhD there will be several compulsory courses, among which courses on programming techniques, parallel programming and programming on heterogeneous hardware, intended to provide the necessary advanced knowledge for carrying out the PhD project.
Concerning the proposed research activities, these consist mainly of three areas of interest:
• Evaluation of RISC-V architectures (e.g. new processors developed in the European Union), with particular attention to efficiency, energy consumption and performance on both data-centric and traditional HPC workflows, in addition to the study of modern ad-hoc algorithms for this kind of architectures;
• Evaluation and analysis of performance and programming models (in-network computing) on smartNIC, with particular attention to training workflows and development of Neural Networks for Artificial Intelligence;
• Evaluation and study of performance and programming models for architectures equipped with processing in memory, in the context of data-centric applications.
Required/Preferred Candidate Skills and Competencies:
• Programming: Proficient in C, C++;
• Familiarity with Linux
• Fundamental algorithm and data structure for linear algebra
• Experienced with MPI and OpenMP for parallel programming.
• Computer Architecture: Solid understanding of interactions among CPUs, GPUs, memory, and storage
• Network and Interconnect Technologies
• Good knowledge of English.
Contact: flavio.vella [at] unitn.it (Flavio Vella) (UniTrento), antonio.sciarappa [at] leonardo.com (Antonio Sciarappa) (Leonardo SpA)
B - Design of Structural Batteries for Aeronautical Application
The aviation industry encounters a significant challenge in balancing economic growth with environmental sustainability. In response, European policy roadmaps have led to consistent long-term research efforts. One strategy to limit greenhouse gas emissions and pollutants during flights is to increase the use of electrical energy onboard aircraft for both propulsive and non-propulsive (secondary systems) purposes. This has led to the concepts of "more electric aircraft," "hybrid electric aircraft," and "all-electric aircraft." The electrification of aircraft can also reduce operational and maintenance costs and create new markets that aren't currently served by conventional aircraft, like electric vertical take-off and landing vehicles for urban air mobility. The use of electrical energy is crucial in enabling distributed electric propulsion concepts and benefiting from a highly energy-efficient electric drive train. However, it is also a limiting factor for the widespread adoption of electrified aircraft, as current battery technologies have limited energy and power density, resulting in a significant weight penalty. To overcome this challenge, an alternative approach is to create multifunctional structures that combine energy storage and load-bearing capabilities. Such structures can act as a replacement for conventional battery systems and structural components, reducing the overall system weight. These types of batteries are commonly known as "structural batteries". Structural batteries are a type of technology that can serve as both electrochemical energy storage system and support for mechanical forces during operation. This is made possible by the dual mechanical and electrochemical properties of carbon fibres that are commonly used in aerospace composites. To combine these properties, solid-state electrolyte is used as binding matrix. The purpose of this technology is to increase the range of battery-powered airborne vehicles, such as drones, and also to provide alternative means of storing electrical energy in airplanes.
To develop this technology, a three-year doctoral program is required aiming at covering several aspects and needs including designing, synthesizing, and assessing electrolytes and separators. Testing will be carried out at the component level, and the application of this technology in aeronautical applications will be evaluated.
Structural batteries do not exist independently of the object they are powering. Instead, the battery is integrated into the object itself. Unlike conventional batteries, which are easily identifiable, structural batteries do not have a clear separation between the battery and the object it powers. For this reason, this technology could, in principle, be used to create cabin walls, floor panels, and even wings. Like conventional lithium-ion batteries, structural batteries in aviation wouldn’t be nearly energy-dense enough to deliver the large amounts of power required by an aircraft propulsion system. But for other uses, such as cabin electronics and avionics, structural batteries could reduce the load placed on the main battery pack and engines without adding extra weight.
Taking into account these considerations, this proposal aims at defining a PhD doctoral program in the field of structural batteries starting from the synthesis of materials at the laboratory scale to cell fabrication and testing and verifying and validating their use in aeronautical applications.
The Ph.D. program is a three-year project. This program offers a unique opportunity for students to acquire new essential skills while personally and professionally developing. It is not just an educational experience but also an opportunity for collaboration and sharing. By collaborating with the Advanced Power & Energy System team of researchers in Leonardo Labs, students can share their ideas and projects, which benefits both the students and the Leonardo Labs by providing new knowledge and skills.
Required/Preferred Candidate Skills and Competencies:
Master in Chemical Engineering, Chemisrty, Materials science or similar.
Knowledge in data processing and analytics
A good level of spoken and written English.
A team player who can collaborate with other researchers, groups.
Demonstrated self-motivation and autonomy.
Contact: vincenzo.sglavo [at] unitn.it (Vincenzo Maria Sglavo )(UniTrento), grazia.accardo [at] leonardo.com (Grazia Accardo) (Leonardo Spa)
Adige S.p.A. - 1 scholarship
C - Model Development or Digital Twin for simulation and data synthesis for critical evaluation in supply chain
The global supply chain industry faces numerous challenges, including inefficiencies, delays, and a lack of real-time visibility. This PhD research proposal aims to develop a comprehensive Digital Twin model to optimize supply chain processes, improve decision-making, and enhance operational efficiency. The Digital Twin will serve as a dynamic, real-time digital representation of the physical supply chain, integrating data from IoT sensors and Manufacturing Execution Systems (MES).
The primary objectives of this research are fourfold: firstly, to develop a robust Digital Twin model that accurately reflects the physical supply chain and allows for scenario simulation; secondly, to integrate real-time data MES (SAP MII) systems to provide continuous monitoring and feedback; thirdly, to implement advanced AI and machine learning algorithms to analyze this data and suggest optimizations; and finally, to evaluate the performance of the Digital Twin in enhancing supply chain efficiency and responsiveness.
The methodology involves an extensive literature review, followed by the design and implementation of the Digital Twin model and simulation environment. AI models will be developed and trained for predictive analytics and optimization, with simulations conducted to test the system's effectiveness. Expected outcomes include improved real-time monitoring, predictive maintenance, optimized operations, and a scalable model adaptable to various industries.
This research is poised to significantly impact the supply chain industry by enhancing efficiency, reducing costs, and improving customer satisfaction. Additionally, it will contribute academically by advancing knowledge in Digital Twin technology and optimization and its applications in supply chain management, providing a foundation for future research in integrating emerging technologies in this field.
Required/Preferred Candidate Skills and Competencies:
Candidates for this research should possess a strong foundation in computer science, with expertise in AI, and machine learning. Essential skills include data integration, real-time systems, and software development, with proficiency in programming languages such as Python. An understanding of supply chain management principles will be beneficial. Additionally, candidates must exhibit strong analytical and problem-solving abilities, capable of designing and implementing complex systems. Beyond technical expertise, curiosity and a passion for innovation are crucial. Successful candidates will be driven by a desire to explore new ideas, ask insightful questions, and pursue novel solutions, continuously learning and adapting to emerging trends in the field.
Contact: giovanni.iacca [at] unitn.it (Giovanni Iacca) (UniTrento), Alberto.longobardi [at] blmgroup.it (Alberto Longobardi) (Adige SpA)
Opt SurgiSystem S.r.l. - 1 scholarship
D - The operating Table as an intelligent hub for data-logging to promote physical patient comfort during surgery
The PhD candidate will dedicate time to research at the company and reference hospitals to analyze the context in the operating room, understanding the technologies used, surgical procedures, regulatory requirements, and OPT development strategies. This knowledge will form the necessary foundation to integrate IoT technologies into the operating table, in line with the competitive analysis and innovations requested by industry professionals.
Required/Preferred Candidate Skills and Competencies:
1. IoT Technical Expertise: Knowledge of Internet of Things technologies, including sensors, actuators, communication networks, and data platforms.
2. Medical Sector Knowledge: Familiarity with the clinical environment, particularly operating rooms, and surgical procedures to understand the needs and dynamics of the context where the technologies will be applied.
3. Understanding of Regulatory Requirements: Knowledge of the regulations applicable in the healthcare sector, which govern the safety of devices, patient data protection, and the approval of new health technologies.
4. Analytical Skills: Ability to conduct research, analyze data, and develop strategies based on the state of the art and user needs.
5. Problem Solving: Skill in solving complex problems creatively and effectively, especially those that arise during the integration of technologies into critical environments like operating rooms.
6. Design Skills: Ability to design technical solutions that are practical, effective, and align with operational and strategic needs.
7. Communication Skills: Essential for collaborating with a multidisciplinary team, effectively communicating with non-technical professionals such as doctors and nurses, and for presenting ideas and progress to various stakeholders.
8. Project Management: Ability to plan, organize, and manage resources while keeping the project within timelines and budgets, as well as adaptability to changes and unforeseen events.
9. Professional Ethics and Privacy Sensitivity: Important when handling sensitive data and working in environments where patient health and safety are a priority.
Contact: giandomenico.nollo [at] unitn.it (Giandomenico Nollo) (UniTrento), personale [at] opt-ita.com (Gatto Antonio) (Opt SurgiSystem S.r.l.)
FBK in collaboration with AWorld Srl Società Benefit - 1 scholarship
E - Transformative AI-Driven Gamification: Promoting Pro-Environmental Behaviors and Sustainability Awareness
We are seeking highly motivated and ambitious candidates to join our research team and pursue an industrial PhD in collaboration with the Motivational Digital Systems research unit of Fondazione Bruno Kessler and AWorld.
Aworld is the official platform of ACTNOW, the United Nations Campaign aiming for individual action on climate change and sustainability. Therefore, the PhD candidate will have the unique opportunity to make an impact with their research to awareness and behavioral change related to environmental sustainability.
The PhD Grant endeavors to explore the transformative potential of Artificial Intelligence (AI) applied to motivational gamified systems, with a particular focus on raising individuals' awareness of environmental sustainability and promoting positive long-term behavioural change.
In recent years, the rapid evolution of AI models and techniques has presented unprecedented opportunities for enhancing personalised and immersive gaming experiences. Motivational gamified systems, designed with educational, awareness-raising, or behavioural change objectives in mind, stand to benefit significantly from the integration of AI-driven capabilities.
This PhD project aims at harnessing AI to revolutionise player profiling, content generation, adaptation, and personalization within gamified systems and experience. By leveraging AI technologies, motivational gamified systems can dynamically adapt to individual player profiles (e.g., preferences, performances, skills, motivational factors), and fosteri deeper engagement and immersion.
The PhD Grant aims to address the following key objectives:
• Methodologies for pro-environmental gamified systems design: define methodologies and design principles for pro-environmental gamified systems that enable sustained user engagement over time (retention), studying methods to prevent fatigue and maintain high interest.
• AI-driven Game Analytics and Impact Assessment: study and develop gamification analytics to analyse user system usage, monitor progress, measure impact achieved, and evaluate motivational factors influencing user behavior.
• AI-Driven User Profiling and Content Generation: leverage AI-based techniques for user profiling and the creation of highly personalised experiences. This includes personalization of motivational elements such as storytelling, challenges, rewards, and feedback, based on individual user profiles and historical data for enhanced engagement and motivation.
• Experimental studies and validation: implement the above-mentioned AI-driven algorithms and tools aimed at promoting eco-friendly behaviors, raising awareness about environmental issues (such as climate change, pollution, conservation of resources), and encouraging action, and validate them within the AWorld platform.
Through interdisciplinary research, the PhD Grant aims to push the boundaries of AI-driven Game User Research, leading to the creation of a new generation of motivational gamified systems that are not only entertaining but also educational, impactful, and deeply immersive.
Keywords: Artificial Intelligence; Game Design; Game User Research; Serious Games; Player Profiling; Adaptive Gameplay; Game Analytics; Human-AI Collaboration; Content Generation; Recommendation; Interdisciplinary Research; Environmental Sustainability; Education.
Required/Preferred Candidate Skills and Competencies:
• Master degree in Computer Science, Cognitive Science;
• Scientific interest in the following areas: data science, machine learning, game design, games user research, artificial intelligence for games, player modeling, game analytics and game studies;
• Oral and written proficiency in English (A2 level);
• Attitude to work in a collaborative environment
• Capability to work in a project-oriented manner, with a strong commitment to achieve assigned objectives and attention to deadlines.
Contact: marconi [at] fbk.eu (Annapaola Marconi) (Fbk), giuseppe.v [at] aworld.org (Giuseppe Valetto) (AWorld Srl Società Benefit)
FBK in collaboration with UFI Hydrogen - 2 scholarships
F - Development of innovative Porous Transport Layers (PTL) for low-temperature electrolysis
PTLs have an important role in low-temperature electrolysis cells, being responsible for water/electrolyte distribution, gas (oxygen and hydrogen) removal, charge transfer, and mechanical support for the membrane. Therefore, their structural properties play an important role in defining the performance of the electrolysis cells, leading to the need of optimizing their porosity, pore diameters, pore structure, morphology, thickness, and permeability.
Thus, this work focuses on the optimization of the PTL properties and adopting both innovative methods for PTL productions, with focus on production methods such us tape casting and more established methods like tape casting or innovative ones.
The first part of the works will focus on literature review, to better understand the state of the art of PTL production and characterizations. Then, the work will focus on PTL structural optimization (for instance including flow field). The activities will include development and characterization of small size PTLs (25 cm2) in FBK and UFI laboratory facilities, as well as modelling and simulation of PTL to optimize their mass transfer, charge transfer and structural properties. Additionally, particular attention will be dedicated to the interactions at the interface between the PTL and the CL.
The second part of the work will focus on the optimization of the production methods, optimizing the production process for scale- production. This phase will consider the optimization of the process on both a technical and economic point of view.
Required/Preferred Candidate Skills and Competencies:
• MSc in Engineering (energy, chemistry, materials, mechanical or similar), or Chemistry or Physics.
• Experience in metallurgy, mechanical proprieties of metal and main mechanic processing
• Experience in electrochemical testing of electrocatalyst in acid and alkaline environments.
• Know-how in processes and methodologies for design of experiment.
• Familiarity with chemical lab procedures.
• Teamwork approach, good communication and relational skills.
• Good knowledge of written and spoken English.
• Self-motivation and result orientation.
• Proven experience in the Hydrogen sector, focused on hydrogen production technology (electrolyzer, methane cracking and SMR), hydrogen handling (transport, distribution, and storage) and hydrogen uses (burner, boiler, fuel cell, etc).
Contact: testi [at] fbk.eu (Matteo Testi )(Fbk), giorgio.ercolano [at] it.ufifilters.com (Giorgio Ercolano) (Ufi Hydrogen)
G - Development of electrodes for Oxygen Evolution Reaction (OER) in PEM electrolysis cell with Catalyst-Coated Substrates (CCS) methods
Electrodes, including a Catalyst Layer (CL) and a Porous Transport Layer (PTL), have an important role in determining the performance and durability of a low-temperature electrolysis cell, making their optimization of primary importance. The composition and microstructure of the anodic CL promoting the OER, that is the rate determining step in PEM electrolysis, is particularly critical since it defines the overall cell efficiency and durability. At SoA, the anodic catalyst is mainly IrOx, which is deposited on the membrane, with the Catalyst Coated Membrane (CCM) method.
This work aims at the development of innovative electrodes with the CCS method, depositing the catalyst on the PTL. The first part of the work will focus on literature review, to better understand the state of the art of OER catalysts for PEM electrolysis. The work will then focus on the optimization of catalysts, inks and processes for CCS preparation. Small electrodes (25 cm2) will be developed in FBK and UFI laboratory facilities. The catalyst will be synthesized with innovative techniques, such as Physical Vapor Deposition (PVD) technique. Particular attention will be paid to the reduction of the loading of PGM (Platinum Group Material).
The electrodes will be first characterized by a morphological and electrochemical point of view, to assess its activity and stability. Finally, selected electrodes will be tested in real operating conditions, to assess their performance in an electrolysis cell.
Required/Preferred Candidate Skills and Competencies:
• MSc in Engineering (energy, chemistry, materials, mechanical or similar), or Chemistry or Physics.
• Experience in electrochemical testing of electrocatalyst in acid and alkaline environments.
• Master thesis on OER-HER catalyst
• Experience in electrochemical characterization (EIS, LSV, etc) and data analysis.
• Know-how in processes and methodologies for design of experiment.
• Familiarity with chemical lab procedures.
• Teamwork approach, good communication and relational skills.
• Good knowledge of written and spoken English.
• Self-motivation and result orientation.
• Proven experience in the Hydrogen sector, focused on hydrogen production technology (electrolyzer, methane cracking and SMR), hydrogen handling (transport, distribution, and storage) and hydrogen uses (burner, boiler, fuel cell, etc).
Contact: testi [at] fbk.eu (Matteo Testi )(Fbk), giorgio.ercolano [at] it.ufifilters.com (Giorgio Ercolano) (Ufi Hydrogen)
FBK in collaboration with Dedagroup SpA - 2 scholarships
H - Securing Digital wallets in complex ecosystems
Making data-driven decisions is widely accepted among private companies. Identifying relevant data for making appropriate business decisions is crucial. High-quality data is essential for effectively using advanced techniques in decision-making processes, such as data analytics and machine learning. Despite the consensus on the data-driven approach in the private sector, the broader context of society (including organizations, groups, informal communities, and citizens) lacks an adequate technological infrastructure to share and use data securely and reliably in a privacy-preserving manner while ensuring that appropriate data quality requirements are satisfied. To address these and related issues, the European Commission initiated the creation of Common European Data Spaces (CEDS), a type of legally regulated complex ecosystems, in various strategic areas (such as healthcare and agriculture) to achieve the key objectives identified in Europe’s political, economic, and social strategy.
CEDSs aim to enable reliable and secure data that can be exchanged across the EU, allowing public and private sector operators to control and share the data they generate while integrating processes for data quality management to allow for the creation of innovative services based on advanced data processing techniques and, most prominently, AI algorithms. This vision requires the development of a technology infrastructure that supports CEDSs and related data processes by integrating cloud and edge computing with networking, protected by security, privacy, trust, and data quality management services to enable the development and maintenance of “fit for purpose" data processes. The cornerstone of all such services is the adoption of digital identity management solutions based on the European Digital Identity (EUDI) Wallet to permit identification, authentication, and authorization of data access, auditing, and data lineage/provenance activities within and among CEDS.
By considering the complex security concerns in this domain, the candidate will be asked to develop security testing methodologies tailored to the specific challenges posed by EUDI wallets when used in different use case scenarios including, in particular, finance and banking. More concretely, the candidate will focus on researching and implementing robust testing frameworks and techniques tailored to payment service provider apps and integrated banking app software development kits. This research contributes to enhancing the resilience of digital wallet ecosystems, securing identities, and improving trust in digital identity management practices.
Required/Preferred Candidate Skills and Competencies:
Master degree in Computer Engineering, Computer Science, or related disciplines;
Know-how on cybersecurity and digital identity management;
Knowledge of methodologies and tools for the security assessment of identity management systems;
Preferred: Good programming skills.
Contact: ranise [at] fbk.eu (Silvio Ranise) (Fbk), pietro.dematteis [at] dedagroup.it (Pietro De Matteis) (Dedagroup SpA)
I - Extending DevSecOps for securing the microservice lifecycle
Created as an extension of the DevOps methodology, DevSecOps adds security constraints to cloud-native application deployment and lifecycle maintenance. By fostering teamwork and automating checks, it speeds up delivery while keeping software safer, ensuring the use of security best practices in each phase of the software development.
Cloud-native applications are fundamentally a new approach to designing and building scalable software based on microservices that run on dynamic environments such as public, private, and hybrid clouds. This new approach raises a completely new set of security challenges, not only concerning the software itself but also its deployment and maintenance. Issues such as insecure cloud configuration, container orchestration mishaps, or insecure secrets storage are few examples of the main security threats mentioned in the OWASP Cloud-Native Application Security Top 10 [1].
In the context of cloud-native applications development, the candidate will be asked to explore the validation and verification of a set of security constraints in the container lifecycle, starting from container image creation and going through the entire execution phase with the goal of identifying vulnerabilities that could make the microservice or its dependable infrastructure insecure by deploying a validator to scan the microservice code and configuration, periodically searching for vulnerabilities and other potential security threats. For this, the candidate will explore several techniques, including Natural Language Processing (NLP) and other machine learning algorithms. The candidate will also consider the application's overall security by implementing a component, integrated into CI/CD pipeline, able to fix the code, recreate the container image, or reconfigure it whenever a threat is identified.
References
[1] https://owasp.org/www-project-cloud-native-application-security-top-10
Required/Preferred Candidate Skills and Competencies:
Knowledge of Computer Networking and Cloud Computing.
Knowledge of Deep Learning and LLM libraries.
Basic knowledge of code best practices and Continuous Integration/Continuous Delivery (CI/CD).
Basic knowledge with Kubernetes, DevOps or DevSecOps tools.
Basic knowledge of Security Vulnerability Assessment.
Contact: testi [at] fbk.eu (D)dsiracusa [at] fbk.eu (omenico Siracusa) (Fbk), pietro.dematteis [at] dedagroup.it (Pietro De Matteis) (Dedagroup SpA)
FBK in collaboration with SI-MEDIA srl - 1 scholarship
J - Artificial intelligence techniques for automatic image/video analysis
The aim of this research project is to develop a system which, inserted into a television automation platform, is capable to automatically identify the highlights of an input video.
The goal of this project is to be able to generate and then automatically publish the result on VOD/OTT platforms (I.E.: Website, APP, HBBTV, Social Platforms).
Highlights generally mean those passages of a video which, once identified, are able to describe the most salient moments of the original video.
The system must be able to recognize the highlights based on video topology. In case of "sport-football", it will therefore have to recognize, for example, yellow and red cards as well as goals, while in “sport-tennis” it will have to be able to recognize the end of a set and the salient events based on the sentiment of the video.
For contents for which it is not possible to give a precise subcategory, it is therefore foreseen to recognize the salient passages based only on the sentiment of the video. Example, during a television talk show, recognize when the discussion becomes more heated.
The analysis carried out by the AI system will have to assign scores to the extrapolated sequences. The score indicates how important that particular segment is, in relation with the complete video, so as to allow the system that analyzes the result to be able to choose the best highlights.
Required/Preferred Candidate Skills and Competencies:good knowledge of machine learning / deep learning algorithms and techniques, especially for video analysis, computer vision and natural language processing; good programming skills (Python, ...).
Contact: poiesi [at] fbk.eu (Fabio Poiesi) (Fbk), Andreas.panozzo [at] si-media.tv (Andreas Panozzo) (SI-MEDIA)
Bluetensor Srl - 1 additional scholarship
K - Research and development of advanced technologies in the field of tomography with the support of AI, applied to space missions
BlueTensor, in collaboration with Tec-Eurolab, is pleased to present an innovative scholarship dedicated to research and development of advanced technologies in the field of tomography with the
support of AI, applied to space missions. This initiative aims to support young researchers who wish to explore new technological frontiers, significantly contributing to the advancement of techniques
for the analysis and interpretation of tomographic data of components to be used in extreme environments.
Contact: massimo.pellizzari [at] unitn.it (Massimo Pellizzari) (UniTrento), federico [at] bluetensor.ai (Federico Lucca )(Bluetensor)
Biocentis s.r.l.- 1 PhD Executive
AE - A realistic modeling and simulation software framework for the analysis of insects population dynamics
Biocentis, a biotech company and spin-out of the Imperial College, is building solutions for controlling insect pests recognized as dangerous to humans in agriculture and the public health domain. Biocentis’ approach is based on releasing genetically modified insects that can impact the wildtype population's fertility, reducing the population size and harm it can cause. Biocentis is committed to developing a flexible agent-based modeling and simulation framework equipped with advanced analytics capabilities to support the company development roadmap in two areas: 1) generate tailored numerical evidence of Biocentis technologies performances both for internal users (i.e., molecular biologists, business developers) and externals (e.g., regulators, policymakers, etc.). 2) Develop an interpretable, personalized, and optimal intervention strategy to inform Integrated Pest Management systems.
To this end, it is mandatory to simultaneously model and simulate both the spatial dispersal of the insect population over a scalable territory, insects’ space-mediated interactions with the environment, and to track the spreading of transgenic constructs in the population across generations. To implement a cost-efficiently control strategy of the target insect, an optimal and adaptive release strategy is required. The strategy will suggest both the release size, the release frequency, and the release location. The strategy is obtained by solving a multi-objectives optimization problem that exploits heterogenous environmental data. The simulation of massive populations and the optimization of time-dependent systems present multiple interdisciplinary challenges on the edge between applied mathematics, computer science, computational biology, and AI. The project will tackle these challenges and it will increase the maturity of current modeling and simulation frameworks. The project will be divided in two distinct phases. The primary outcome of Phase I will be tools to 1) simulate different spatiotemporal scales, 2) simulate different insect species, and 3) study the interaction between genetic control technologies and other intervention approaches. Examples include parasitoids, bacillus thurigiensis crops, and synthetic and organic pesticides. Phase II will entail the development and integration of new techniques to generate ad-hoc optimal release strategies. We will investigate variants of well-established techniques (e.g., genetic algorithm, reinforcement learning) and advanced approaches (e.g., physics-informed reinforcement learning). Also, this phase will augment the framework's Technology Readiness Level (TRL) by implementing proper interfaces with external data sources (weather stations, satellite data, etc.) and user interfaces.
Drosophila Suzuki (SWD) population data will validate the modeling and simulation framework. This invasive fruit fly originated from East Asia and is causing massive production losses in all areas recently introduced, including the Trentino region. In addition to SWD, upon availabilities, Aedes Aegypti data will be used to validate the modeling and simulation framework.
Required/Preferred Candidate Skills and Competencies:
• Master’s degree in applied mathematics or computer science
• Proved expertise in mathematical modelling applied to anthropoids.
• Good knowledge of insect control techniques and their computational modelling.
• Good knowledge of both Ordinary and Partial Differential Equations.
• Good knowledge of Agent Based Modelling and Simulation (flameGPU preferred)
• Professional experience in Software Development (>5 years)
• Familiar with git source control system
• Expert in Phyton, C++ coding languages
• Basic knowledge in AWS cloud deployment
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: andrea.pugliese [at] unitn.it (Andrea Pugliese), giovanni.iacca [at] unitn.it (Iacca Giovanni) (UniTrento), matteo.rucco [at] biocentis.com (Matteo Rucco) (Biocentis srl)
Bioniks srl - 1 PhD Executive
BE - Bacterial cellulose-derived aerogels for energy storage and environmental applications
Bacterial cellulose (BC) is a highly porous nanofibrillar cellulosic material produced by certain bacteria. Its unique structural properties make it an excellent precursor for the production of ceramic aerogels - extremely lightweight porous carbon materials with high surface areas and tunable pore architectures. This doctoral research aims to develop optimized synthetic protocols for converting purified BC pellicles, provided by the collaborating company Bioniks Srl (Verona, Italy), into aerogels with tailored microstructures and compositions for targeted applications in energy storage and water remediation.
The initial phase will focus on studying the pyrolysis and chemical/physical activation conditions to control the specific surface area, total pore volume, and hierarchical pore architecture of the BC derived carbon aerogels. Prior to pyrolysis, the BC hydrogels or aerogels may be subjected to impregnation treatments in various media to incorporate additional elements (e.g., inorganic salts, MXenes), thereby producing aerogels with diverse chemical compositions and robust 3D structures. This will involve systematic investigations into activation agents, temperatures, dwell times, controlled gaseous environments, and soak solutions. Comprehensive materials characterization using N2 sorption, electron microscopy, Raman/FTIR spectroscopy, X-ray diffraction, and SEM/EDX will guide the optimization.
The performance of the optimized BC-derived aerogels will then be evaluated for thermal energy storage applications, wherein composites incorporating organic phase change materials, such as paraffins and fatty alcohols, will be explored. Furthermore, the aerogels' adsorption capacities for various water contaminants including heavy metals, dyes, and organic pollutants will be quantified using batch adsorption studies. Their potential for asymmetric separations of oil/water mixtures and desalination applications will also be probed by examining their wetting properties and performing separation experiments.
The research outcomes will provide fundamental insights into optimized aerogel synthesis protocols from BC while developing high-performance carbonaceous and heteroatom-doped porous materials for sustainable energy storage and water remediation applications.
Required/Preferred Candidate Skills and Competencies:
- Good knowledge of bacterial cellulose chemical, thermal, mechanical, structural properties and of bacterial cellulose interaction with other materials
- Experience in handling, treating, analyzing bacterial cellulose
- Good knowledge about polymeric materials, their processing, properties, and manipulation
- Good knowledge and experience with material characterization and functionalization techniques
- Good knowledge of written and spoken English
- Ability to write technical reports and scientific papers
- Master’s degree in materials engineering (or equivalent)
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: giandomenico.soraru [at] unitn.it (Gian Domenico Sorarù), giulia.fredi [at] unitn.it (Giulia Fredi) (UniTrento),martina.bruschi [at] bioniks.it ( Martina Bruschi )(Bioniks srl.)
Ducati Motor Holding S.p.A. - 1 PhD Executive
CE - Autonomous Robot Design and Implementation for Racetrack High-performance Mapping
Simulating a racing motorcycle in its working environment helps to improve vehicle’s performances, especially if reliable and up-to-date data are used. The purpose of this research project is to further develop the measuring platform in use today to achieve a fully self-driving robot that reconstructs the circuit in 3D with the desired maximum target uncertainty. Besides an accurate model of the novel robotic platform, which uses sensor fusion algorithms for the circuit reconstruction, the project foresees an investigation of machine learning algorithms to properly detect people, static objects or operating machines on the track, to safely avoid them, and still performing the track reconstruction with the rated maximum target uncertainty. Therefore, the project will build upon the seamless integration of AI solutions with classic model-based design to safely navigate the on-purpose designed robotic platform on the rack, retrieve the necessary information and reach the desired track reconstruction accuracy.
Required/Preferred Candidate Skills and Competencies:
- Recently completed or will soon complete master studies in mechatronics or robotics engineering.
- Good programming skills in Matlab, Simulink and Linux-based system programming.
- Experience in developing mobile robotics with the integration of LiDAR sensor, DC motors, CAN-BUS communication.
- Good collaboration in a small team and efficiently communication skills.
- Ability to combine conceptual thinking with strong technical skills.
- Fluent in English.
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: daniele.fontanelli [at] unitn.it (Daniele Fontanelli) (UniTrento), andrea.pierantoni [at] ducati.com (Andrea Pierantoni) (Ducati Motor Holding s.p.a.)
FBK- 2 PhD Executive
DE - Driving Innovation in Edge Computing and IoT with MLOps
The intersection of Edge Computing, the Internet of Things (IoT), and Machine Learning Operations (MLOps) presents a transformative opportunity to enhance computational efficiency, data processing, and decision-making in real-time applications. This research seeks to address the critical challenges at this intersection, such as latency, privacy, and scalability, by harnessing the power of MLOps to innovate within Edge Computing and IoT environments. The primary objective of this scholarship is to develop a framework that integrates MLOps methodologies into Edge Computing and IoT systems, aiming to improve the deployment, monitoring, and management of ML models at the edge of the network. This framework will be designed to support dynamic adaptation to changing data streams, ensure data privacy and security, and facilitate efficient resource utilization.
The outcome will not only contribute to the academic body of knowledge but also offer practical guidelines for industry practitioners looking to leverage the benefits of AI at the edge.
Contact: roberto.passerone [at] unitn.it (Roberto Passerone) (UniTrento), mvecchio [at] fbk.eu (Massimo Vecchio) (FBK)
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
EE - AI and Human Skills – How Artificial Intelligence Is Transforming Human Capital
Artificial intelligence (AI) and robotics are reshaping the narrative about job automation and displacement, highlighting the need for a workforce that is adaptable, skilled, and prepared for the evolving demands of the job market. At the same time AI has the potential to assist workers in the acquisition of new skills and revolutionize education by providing interactive tools for personalized learning experiences.
The Research Institute for the Evaluation of Public Policies (FBK-IRVAPP) seeks to address the societal impact of these new technologies and their policy implications for work and education. This research project will explore the role of artificial intelligence for human capital from two perspectives:
1) a workforce dynamics perspective, assessing how AI and automation are changing the demand for workers’ skills and understanding how artificial intelligence can complement human skills
2) an educational perspective, examining how AI can assist humans in acquiring new skills and evaluating the effectiveness of AI-powered educational tools in providing personalized learning experiences.
The PhD candidate will conduct empirical research, focusing on counterfactual analysis of the causal effects of policy interventions in these areas.
Required/Preferred Candidate Skills and Competencies:
- Master’s degree in Economics, Social Sciences, Public Policy, or similar.
- Knowledge of public policy evaluation methods.
- Demonstrated interest in the societal impact of artificial intelligence.
- Proficiency in R or Stata and Microsoft Excel.
- Oral and written proficiency in Italian and English.
- Strong communication and writing skills.
- Willingness to work effectively in a team, as well as independently.
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: Michele.fedel [at] unitn.it (M)mauro.caselli [at] unitn.it (auro Caselli) (UniTrento), azzolini [at] irvapp.it (Davide Azzolini) (FBK)
Huawei Technologies Duesseldorf GmbH - 2 PhD Executive
FE - System security for next-generation computing
The PhD candidate will contribute to the research, development, publication and product adoption of system security technologies for next-generation heterogeneous computing architectures and models for AI and scientific computing such as based on NPU and GPU, working on real world problems for a wide range of Huawei products and services to preserve and prove their trustworthiness through technology, from a unique research position connecting Academia and Industry. Potential responsibilities include:
• Assess system security concerns for computations running on xPU (e.g. NPU, GPU, TPU) as part of a heterogeneous computing platform (e.g. for AI, scientific computing etc.)
• Research on innovative concepts and architectures in the field of Trustworthy Computing
• Implement proof of concepts and support technology adoption in products
• Publish research papers and technical reports at top security conferences and collaborate with academic partners
• Improve, by design, the end to end system security capabilities of future Huawei products
Required/Preferred Candidate Skills and Competencies:
• You have recently completed or will soon complete your master studies in computer science, information technology, cybersecurity, electrical engineering, or mathematics
• You have research experience in system security, applied cryptography, computing engines such as GPU or NPU
• Experience in one or more of the following is a plus: compilers, AI framework design (full stack), memory safety, TEEs
• You have good programming skills in Rust, C, C++, Go and in system programming
• You have demonstrated affinity for concept design and hands-on validation
• You have a passion for finding solutions for complex technology issues
• You have excellent collaboration and communication skills
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact:bruno.crispo [at] unitn.it ( Bruno Crispo) (UniTrento),silviu.vlasceanu [at] huawei.com ( Silviu Vlasceanu) (Huawei Technologies Duesseldorf Gmbh)
GE - System security for heterogeneous platforms
The PhD candidate will contribute to the research, development, publication and product adoption of system security technologies for heterogeneous computing platforms and clusters targeting AI and compute, working on real world problems for a wide range of Huawei products and services to preserve and prove their trustworthiness through technology, from a unique research position connecting Academia and Industry. Potential responsibilities include:
• Assess system security and platform resilience risks for the generative AI and high-performance computing infrastructures deployed in datacenters and computing clusters, but also in consumer heterogeneous computing devices
• Research on innovative concepts and architectures in the field of Trustworthy Computing
• Implement proof of concepts and support technology adoption in products
• Publish research papers and technical reports at top security conferences and collaborate with academic partners
• Improve, by design, the end to end system security capabilities of future Huawei products
Required/Preferred Candidate Skills and Competencies:
• You have recently completed or will soon complete your master studies in computer science, information technology, cybersecurity, electrical engineering, or mathematics
• You have research experience in system security, applied cryptography, computer architectures, OS design
• Experience with compilers, enclave engines, TEEs, secure processing architectures and accelerator designs is a plus
• You have good programming skills in Rust, C, C++, Python, Go and in system programming
• You have demonstrated affinity for concept design and hands-on validation
• You have a passion for finding solutions for complex technology issues
• You have excellent collaboration and communication skills
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: caterina.zanella [at] unitn.it (B)bruno.crispo [at] unitn.it (runo Crispo) (UniTrento),silviu.vlasceanu [at] huawei.com ( Silviu Vlasceanu) (Huawei Technologies Duesseldorf Gmbh)
Rotho Blaas S.r.l. - 1 PhD Executive
HE - Impact of Industrial Production and Service Conditions on Hydrogen Absorption in Timber Screws
Rothoblaas is an Italian multinational company specialized in the development and supply of high-tech solutions for the construction industry.
As the mass timber construction industry continues to grow, so does the use of wood screws in extreme structural applications requiring high levels of mechanical performance. This trend leads to unique challenges in fasteners design, installation, and material performance.
A crucial aspect of timber screw manufacturing involves employing electrodeposited coatings on carbon steel screws to prevent corrosion, with the additional concern that hydrogen absorption can occur during this process.
The research project aims to address these challenges by focusing on understanding the parameters related to hydrogen absorption during fasteners manufacturing and analyzing products under various application conditions.
The mechanical properties of wood screws will be thoroughly examined, along with an evaluation of installation procedures to ensure optimal strength of connections. This research work aims to improve the structural integrity and performance of timber construction supplies, integrating rigorous quality control measures and innovative solutions, advancing the industry for the benefit of all interested parties.
The research outcomes could bring the development of guidelines and best practices to mitigate the risks associated with hydrogen absorption and ensure the structural integrity and safety of timber screw connections in various construction scenarios.
Required/Preferred Candidate Skills and Competencies:
• MSc in chemistry, materials science, physics, chemistry engineering or similar
• Advance Knowledge in Corrosion science and protective coatings
• Knowledge of Metallurgy and Industrial manufacturing processes
• Familiarity with chemical lab procedures and equipment
• Ability to write technical reports and scientific papers
• The PhD candidate will be involved in various interdisciplinary fields encompassing conceptualization, development, product innovation, process and testing
• Identifying the optimal testing methodology to assess the durability and applicability of novel solutions for industrial manufacturing
• Good knowledge of written and spoken English
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: Michele.fedel [at] unitn.it (Michele Fedel )(UniTrento), Manuela.chiodega [at] rothoblaas.com (Manuela Chiodega )(Rotho Blaas S.r.l.)
Sybilla Biotech spa - 1 PhD Executive
IE - Characterization of the Effect of Folding Interfering Degraders in cellular model
The proposed project aims to investigate the effects associated with small molecules that induce protein degradation by targeting folding intermediates, specifically focusing on cellular models. Understanding these effects is crucial for the development of safe and effective therapeutic strategies.
Techniques such as proteomics, western blotting, and RT-PCR will be employed to identify and characterize proteins affected by the small molecule-induced protein degradation. Proteomic approaches will enable comprehensive profiling of protein expression changes, while western blotting and RT-PCR will allow validation and quantification of specific targets.
The research will provide valuable insights into the effects of small molecules that induce protein degradation in cellular contexts, shedding light on potential safety concerns and guiding the design of future therapeutic agents.
This project offers an exciting opportunity for a motivated and ambitious PhD candidate to engage in cutting-edge research at the interface of cellular biology and drug discovery. The successful applicant will join a dynamic research team and will have access to state-of-the-art facilities and resources.
Required/Preferred Candidate Skills and Competencies:
We are seeking a highly motivated candidate with a diverse skill set in multiple biological disciplines, including cellular and molecular biology, and immunostaining. In particular, the applicant should have experience in biochemistry and molecular biology techniques, RT-PCR and fluorescence imaging. Competencies in planning and setting up experiments are required. The candidate should be versatile and motivated in working in a team environment.
A background knowledge in drug discovery is also appreciated.
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: Emiliano.biasini [at] unitn.it (Emiliano Biasini) (UniTrento), Tania.massignan [at] sibyllabiotech.it (Tania Massignan)Tania.massignan [at] sibyllabiotech.it ( )(Sybilla Biotech Spa)
Prebiomics Srl - 1 PhD Executive
JE - Conceptualization and manufacturing of different microbiome-based agents for daily home oral hygiene
The oral cavity is home to a variety of aerobic and anaerobic bacteria, fungi, archaea, protozoa, and viruses. The adult oral microbiome becomes a stable community primarily within the first year of life. This early establishment, combined with immune system education, leads to a dynamic balance with the host’s immune system. This mutual recognition and tolerance, along with stable interbacterial relationships, grant the oral ecosystem the ability to resist change.
It has been noted that the oral microbiome reverts to its original state after routine dental cleanings and that subgingival and supragingival biofilms nearly regain their original composition after repeated gingivitis episodes. This stability and resilience are critical for maintaining overall human health.
In the past, researchers focused on the antimicrobial effects of different agents such as toothpastes and mouthwash, against oral disease pathogens in laboratory settings. While this approach has been somewhat effective, it oversimplifies the complexity revealed by our current understanding of the oral microbiome. Modern oral hygiene practices, particularly those involving mechanical brushing, interdental cleaning, and nonsurgical therapy likely represent the most significant factor influencing the shaping of the oral microbiome in modern humans. Traditional approaches supported the idea that eliminating pathogenic species with antiseptic agents ensures good oral hygiene. However, we now know that a healthy oral microbiome consists of a complex and diverse community of microorganisms.
The aim of this research project is to examine the impact of several antimicrobial formulations commonly used in oral hygiene agents (i.e mouthwashes and toothpastes) on the oral microbiome through the use of shotgun metagenomics, a high-throughput sequencing technique that allows comprehensive genomic analyses of all microbes in a sample, including those that cannot be cultured.
The objective is to identify and develop various potential formulations of oral care products (toothpastes and mouthwashes) containing different ingredients. These formulations will be tested in vitro against different microbiome profiles, both healthy and diseased, present in the PreBiomics database. An in vivo study will then investigate their clinical effects and individual responses, guiding us towards the commercialization of the most effective formulations.
This research has the potential to pave the way for bringing precision medicine into the daily oral hygiene routine of every individual, giving each person the opportunity to use the product that best suits their oral microbiome and their state of health or disease.
Required/Preferred Candidate Skills and Competencies:
The ideal candidate should have a solid background in dentistry, a basic knowledge of biomolecular sciences and a basic knowledge in next-generation sequencing and its potential applications in oral health. A degree in dentistry is required with a solid experience in the fields of periodontology and cariology.
The following characteristics are not essential but would be particularly appreciated among the candidates:
- An interest in the applications of precision medicine tools in oral health
- Active collaboration with a specialized laboratory and bioinformaticians as well as the ability to coordinate a clinical observational study
- The ability to integrate research study findings with potential commercial applications within the company environment
- The ability to write papers and technical reports in order to maintain the high scientific profile through which the company develops its products and brings them to market
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: nicola.segata [at] unitn.it (Nicola Segata) (UniTrento), paolo.ghensi [at] prebiomics.com (Paolo Ghensi) (Prebiomics Srl)
Ubitech - 1 additional PhD Executive
KE - Enabling Advanced (Quantum-Secure) Security and privacy Crypto Primitives through HW-based Trusted Computing Abstractions
With the rise of Internet of Things and next-generation smart connectivity systems, the number of devices that run potentially vulnerable software has exploded, and vulnerabilities are increasingly been discovered in both the software hardware of such devices. To keep up with the amount of services that must be vetted for vulnerabilities, an automated approach is required. Alternative, security architectures are required which allow the system to detect whether it is under attack, and therefore intensify the protection mechanisms. Particular focus would be given on control systems, communication and computing infrastructures, and in general both high-end and low-end devices. The envisioned research activities include the design and development of cryptographic schemes and attestation mechanisms for enhancing the overall security and privacy of deployed embedded devices as well as safeguarding their trustworthiness level. Particular focus will also be given on designing an ensemble of crypto algorithms and protocols including key encapsulation, digital signatures, (authenticated) key exchange. Authorization, identity-management, long-term data security, providing a fully functional equivalent of Public Key Infrastructure (PKI) which is robust against future algorithmic and quantum computing advances but also practical enough to be integrated in today’s legacy systems. The endmost goal is to define an interoperable Trusted Computing Base (anchored to secure elements like a TPM and/or TEE) that can provide the necessary optimization and protection for providing such advanced crypto mechanisms.
Required/Preferred Candidate Skills and Competencies:
Candidates should have a two-year Master’s degree or a similar degree with an academic level equivalent to an MSc degree. A good background in the theory and practice of trusted computing is essential, and preference will be given to candidates who can demonstrate knowledge on theoretical and applied crypto and trust management. Good implementation skills and practical experience are also desirable. Furthermore, good command of the English language is essential.
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.
Contact: caterina.zanella [at] unitn.it (B)bruno.crispo [at] unitn.it (runo Crispo) (UniTrento),agiannetsos [at] ubitech.eu (Thanassis Giannetsos) (Ubitech)
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