Three grants are available:

(*) 2 grants funded by Università degli Studi di Genova

(*)1 grant cofunded by MUR/Department (DICCA) and by the Research Centre EURAC

For the  two grants funded by Università degli Studi di Genova, possible research topics are described in the following. Interested Candidates are invited to contact the proposers listed below or other members of the Internal Curriculum Committee for agreeing on other possible topics (the complete list of members is available here).

For the grant cofunded by MUR/Department DICCA and by the Research Centre EURAC, please refer to the Research topic BF1. For this grant the research project of the candidate must agree with the goals identified for this research theme. Moreover, the holder of this scholarship, in accordance with the study plan and any planned period abroad, shall spend (in person) at least 40% of his/her time, even if not continuously, at the funding institution. 

Research topic for the grant funded by the MUR/Department DICCA and by the Research Centre EURAC - BF1

Research topic BF1

Title: Climate Stress Testing for Socioeconomic Vulnerability Recognition in Water Management Systems Under Hydroclimatic Hazards

Proposers: Giorgio Boni (DICCA) and  Massimiliano Pittore, Stefano Terzi (EURAC)

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: The main aim of this project is to develop a decision-support framework for managing mountain water systems exposed to increasing climate-related stress. Starting from the analysis of the 2022 drought event in the Upper Adige River basin, this PhD research extends beyond a single case study to address broader alpine and European contexts.

The research addresses the urgent need to recognize systemic vulnerabilities across interconnected water-energy-food-ecosystem (WEFE) systems when facing compound hot and dry events.

Specifically, the objectives are:

(i)            To quantify hydroclimatic extremes and assess their impacts on water systems.

(ii)          To map sectoral vulnerabilities using participatory impact chain modeling.

(iii)         To develop a flexible climate stress testing framework combining scenario simulations and stakeholder involvement.

(iv)         To analyze WEFE Nexus feedbacks and trade-offs to support resilient governance.

The project will be developed in collaboration with Eurac Research. The holder of this scholarship, in accordance with the study plan and any planned period abroad, shall spend (in person) at least 40% of his/her time, even if not continuously, at the funding institution.

For more information please contact: Giorgio Boni: giorgio.boni@unige.it

Note: the holder of this scholarship, in accordance with the study plan and any planned period abroad, shall spend (in person) at least 40% of his/her time, even if not continuously, at the funding institution. 

Link to the group or personal webpage:

Giorgio Boni: https://rubrica.unige.it/personale/VUZCWlJs

Eurac Research: https://www.eurac.edu/en

References:

o   Albano, C. M., McCarthy, M. I., Dettinger, M. D., et al. (2021). Techniques for constructing climate scenarios for stress test applications. Climatic Change, 164(33).

o   Chiogna, G., Majone, B., Paoli, K. C., Diamantini, E., Stella, E., Mallucci, S., ... & Bellin, A. (2016). A review of hydrological and chemical stressors in the Adige catchment and its ecological status. Science of the Total Environment, 540, 429–443.

o   Colombo, N., Guyennon, N., Valt, M., Salerno, F., Godone, D., Cianfarra, P., ... & Romano, E. (2023). Unprecedented snow-drought conditions in the Italian Alps during the early 2020s. Environmental Research Letters, 18(7), 074014.

o   Fuchs, S., Keiler, M., Sokratov, S., Shnyparkov, A. (2013). Spatiotemporal dynamics: the need for an innovative approach in mountain hazard risk management. Natural Hazards, 68(3), 1217–1241.

o   Napoli, A., Matiu, M., Laiti, L., Barbiero, R., Bellin, A., Zardi, D., & Majone, B. (2025). Review on climate change impacts on the Water-Energy-Food-Ecosystems (WEFE) Nexus in the North-Eastern Italian Alps. Climatic Change, 178(3), 41.

o   Prudhomme, C., Wilby, R. L., Crooks, S., Kay, A. L., Reynard, N. S. (2010). Scenario-neutral approach to climate change impact studies: application to flood risk. Journal of Hydrology, 390(3-4), 198-209.

o   Verbist, K. M. J., Maureira-Cortés, H., Rojas, P., & Vicuña, S. (2020). A stress test for climate change impacts on water security: A CRIDA case study. Climate Risk Management, 28, 100222.

o   Zebisch, M., Renner, K., Pittore, M., Fritsch, U., Fruchter, S. R., Kienberger, S., ... & Delvis, J. L. (2023). Climate risk sourcebook. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH.

Possible research topics for the two grants funded by the University of Genova

Research topic A

Title: Assessment of the Industrial Risk of a New Sustainable Process for Electrodes Production for AEM/ALK Electrolyzers and Development of a SCADA system or AI tool for safe automated operation at reduced exposure to chemicals

Proposers: Ombretta Paladino

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Anion Exchange Membrane Water Electrolysis (AEMWE) is a promising approach for sustainable hydrogen production. Realizing efficient, durable AEM electrolyzers requires ongoing materials innovation and components integration. Both the raw materials and the produced electrocatalysts, polymer membranes, and electrodes are subject to REACH Regulation (EC 1907/2006). 

At ECPLab DICCA (Savona Campus) we are actually working on the development of new materials for AEMWE (Projects: NEMESI, funded by MASE – PNRR, with Ansaldo Green Tech; ARKEL, funded by Compagnia di San Paolo; Collab with Antares Electrolysis and IIT). Our activities have been dedicated to the development of a new sustainable and low risk process for the production of high performance electrodes (patent filed by Unige) without managing powders, in order to highly reduce possible exposure to chemicals. 

We are also studying approaches to recover metals from exhaust components to reintroduce them into the production cycle.

The PhD position is inside this context and will focus on: 

1) Assessment of the Human Health Risk due to chemicals for the proposed production process: all the main chemicals, process units and steps will be considered. The procedure must be carried out at Tier 1, 2 (partially 3), using methodologies for carrying out HHRA under uncertainty (already adopted by the research group).

2) Optimization of the operating conditions on the basis of the results of HHRA and the required electrochemical and transport properties of the produced electrodes (properties giving them stability and duration).

3) Optimization of the fully automated process. A system for data acquisition and control of the main process units already exist, developed in Labview. The student will evaluate one of these possible approaches a) update the existing system in order to be remotely managed, allowing also the process (i.e. all the equipment) to be safely shut-down in case of faults and lack of power supply, and possibly restarted; b) developing an AI tool for early-warning and safe operation, working in parallel with the existing system.

For more information please contact: Ombretta Paladino (paladino@unige.it)

Link to the group or personal webpage:

https://it.linkedin.com/company/ecplab-unige https://dicca.unige.it/laboratori/lab_chimica/ing_processi_chimici

References:

o   O. Paladino, A.Niyati, A. Moranda, P. Beigzadeh Arough, B. Marcenaro. https://doi.org/10.1016/j.applthermaleng.2024.124532

o   A.Niyati, A. Moranda, J.F. Basbis, O. Paladino. DOI: 10.1039/D4NJ01581A

o   O. Paladino, A. Moranda. https://doi.org/10.1016/j.jhazmat.2020.124222

Research topic B

Title: Coastal Flooding hazard under present conditions and in the framework of future climate change

Proposers: Giovanni Besio

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Coastal erosion and coastal flooding are the main hazard for the management of the coastal zone and could pose many disruptions to different human production activities. A detailed knowledge of the processes and on the intensity of the phenomenon it is crucial to provide managing authorities and institutions, and economic stakeholders, the necessary information and scenarios for decision making and policies development.

The research projects will develop a methodological approach suitable for the definition and estimation of extreme levels of coastal flooding due to different physical processes such as wave run-up and set-up, storm surge and long-term sea-level variation due to climate change. The research activities will consist in developing a suitable numerical model suite for the description of wave storms in the coastal region, giving insight of wave run-up and set-up processes, the characterization of storm surge levels and the use of sea level variation projections under different Shared Socioeconomic Pathways (SSPs) for mid and end of the century (2050 & 2100). After developing a wide dataset of metocean forcings the analysis of flooding processes will be carried out through the employment of process based numerical models, having as the final objective of the project the evaluation of return levels for coastal inundation areas along the coastline of the Mediterranean Sea with specific attention to hot spot with respect to coastal infrastructures, lowlands and populated areas.

For more information please contact: Giovanni Besio – giovanni.besio@unige.it

Link to the group or personal webpage: meteocean.science

Research topic C

Title: Developing Decision Support Systems to plan, design and install nature-based solutions in urban areas for the mitigation of the pluvial flooding

Proposers: Anna Palla, Ilaria Gnecco

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Pluvial flooding has become one of the most frequent natural disasters in recent years, and the pairing of nature-based solutions with the traditional grey infrastructure is recognized as the solution to mitigate the negative impact of urbanization on the hydrological response. The main objective of the present research was to develop a Decision Support Systems (DSS) for planning the interventions (nature-based solutions and traditional grey infrastructure) at the catchment scale to enhance urban resilience to cope with intense rain events with an insight into both current and future climate.

For more information please contact:

Anna Palla, anna.palla@unige.it

Ilaria Gnecco, ilaria.gnecco@unige.it

Link to the group or personal webpage:

https://rubrica.unige.it/personale/UkNGWllr 

https://rubrica.unige.it/personale/UkNHWF5o

References:

o   Jabeen, K., Grossi, G., Turco, M., ..., Gnecco, I., Palla, A. Continuous Simulations for Predicting Green Roof Hydrologic Performance for Future Climate Scenarios. Hydrology, 2025, 12(2), 41

o   Tameh, S.N., Gnecco, I., Palla, A. Analytic hierarchy process in selecting Bioretention Cells in urban residential settlement: Analysing hydrologic and hydraulic metrics for sustainable stormwater management. Journal of Environmental Management, 2024, 371, 123142.

o   Palla, A., Gnecco. I. On the Effectiveness of Domestic Rainwater Harvesting Systems to Support Urban Flood Resilience. Water Resource Management, 2022, 36(15), 5897–5914.

o   Palla, A., Gnecco, I. The web-gis TRIG eau platform to assess urban flood mitigation by domestic rainwater harvesting systems in two residential settlements in Italy. Sustainability, 2021, 13(13), 7241.

Research topic D

Title: The environmental risk of urban stormwater discharges from traditional to emerging contaminants

Proposers: Ilaria Gnecco, Anna Palla

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Rapid increase in global urbanization is producing multiple environmental challenges, one being the impacts of urban stormwater runoff in terms of quantity and quality issues. On the other hand, demands on global water supplies are increasing due to several water stressors, consequently urban water authorities are exploring alternative water sources to meet ever-increasing demands, and stormwater represents one of the alternatives. The aim of the research is to improve the understanding of stormwater quality to quantify the environmental and ecological impacts, and treatability, of stormwater. Among the different pollutants associated with stormwater runoff, microplastics have become a major emerging class of pollutants representing significant eco-toxicological risks for ecosystems and marine environments. However, the investigation of microplastic as well as traditional and emerging contaminants in stormwater runoff is still an open issue (including the methodology for their identification and quantification). Therefore, widespread in-depth studies are urgently needed to bridge the knowledge gaps to enable a more comprehensive risk assessment of emerging contaminants in inland waters and support the relevant authorities in developing a policy addressing this issue. In particular this research aims at investigating the role of stormwater control measures with respect to emerging contaminants thus evaluating the source pathway as well as the potential mobility downstream and therefore the long-term fate.

For more information please contact:

Ilaria Gnecco, ilaria.gnecco@unige.it

Anna Palla, anna.palla@unige.it

Link to the group or personal webpage:

https://rubrica.unige.it/personale/UkNGWllr 

https://rubrica.unige.it/personale/UkNHWF5o

References:

o   Tameh, S.N., Drake, J., Palla, A., Gnecco, I. Hydrologic, hydraulic, and water quality characterization from laboratory test and SWMM modelling: A full-scale evaluation of Filterra® bioretention cell for urban stormwater management. Environmental Modeling and Software, 2025, submitted.

o   Gnecco, I., Palla, A., Sansalone, J.J. Partitioning of zinc, copper and lead in urban drainage from paved source area catchments. Journal of Hydrology, 2019, 578, 124128

Research topic E

Title: AI and Met-Ocean Modeling for Risk-Informed Forecasting of Extreme Weather and Water Events

Proposers: Andrea Mazzino

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Extreme weather and climate-related water events such as floods, heatwaves, tropical cyclones, hailstorms, storm surges, extreme wave events are becoming more frequent and intense due to climate change, posing significant challenges to public safety, infrastructure, and resource management. Traditional weather forecasting models, while physically consistent, often struggle to capture the localized and nonlinear nature of such extremes, especially when computational limitations restrict ensemble size or resolution.

This project aims to develop innovative AI-based techniques to enhance the forecasting of extreme events by leveraging the strengths of both numerical weather/wave prediction (NWP/WP) models and modern machine learning architectures. The focus will be on the integration of deterministic forecasts (e.g., from models like WRF/WWIII) with deep learning models capable of generating probabilistic ensemble predictions without requiring multiple physical model runs. The proposed method will explore architectures such as conditional variational autoencoders (CVAEs) trained to predict the full distribution of target variables (e.g., precipitation, lightning, hails, wind gusts) at high resolution. The new idea to explore is to map classical extreme event forecasting into an anomaly detection problem. This is a change of paradigm in the field.

A central aspect of the project will be the use of advanced loss functions for probabilistic forecasting, such as the Continuous Ranked Probability Score (CRPS), and the development of explainability tools to understand AI-driven predictions. Additionally, the project will investigate the synergy between AI and physics by embedding physical constraints or conservation laws within the learning process (so-called Physics-Informed ML).

The developed system will be evaluated on real-world datasets of extreme events using both retrospective and hindcast data (e.g., ERA5, satellite observations, in-house produced regional model outputs). Applications include improved early warning systems and enhanced risk assessment tools, potentially reducing the socioeconomic impact of extreme events

For more information please contact: Andrea Mazzino, DICCA, andrea.mazzino@unige.it

Link to the group or personal webpage: https://meteocean.science

References:

o   Cavaiola, M., Cassola, F., Sacchetti, D. et al. Hybrid AI-enhanced lightning flash prediction in the medium-range forecast horizon. Nat Commun 15, 1188 (2024). https://doi.org/10.1038/s41467-024-44697-2

Research topic F

Title: Development of a meteorological WRF-LES + CFD coupled model chain for the simulation of heat waves and windstorms to increase the resilience of Mediterranean cities to extreme events

Proposers: Massimiliano Burlando, Antonio Parodi

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Cities in the Mediterranean basin are facing growing threats from extreme weather events driven by climate change—particularly heat waves, windstorms, and flash floods (Giorgi 2006; Guida et al. 2022). These phenomena are increasing in both frequency and intensity, posing significant challenges to urban safety, infrastructure resilience, and public health. This research project aims to develop an integrated, high-resolution modeling framework that couples the Weather Research and Forecasting (WRF) model (Lagasio et al. 2019) in Large Eddy Simulations (LES) mode (Bauer et al. 2015) and Computational Fluid Dynamics (CFD) tools (Blocken 2015). The objective is to improve the accuracy and spatial detail of forecasts for extreme events affecting complex urban environments. 

The WRF-LES component will simulate atmospheric processes at fine scales, enabling the capture of turbulence and local variability, while the CFD models will represent urban geometry and atmospheric processes within the urban canopy. By integrating meteorological and urban-scale modeling, the project will deliver a comprehensive simulation chain capable of predicting the localized impacts of extreme weather, from heat stress in dense neighborhoods to wind loads on structures. 

The model will be applied to a case study in a Mediterranean city to identify the typical vulnerabilities and assess potential mitigation strategies. This toolchain will provide valuable insights for urban planners, emergency services, and climate adaptation professionals, contributing to more proactive and data-driven approaches to risk management in cities facing intensifying climate stress.

For more information please contact: Massimiliano Burlando (massimiliano.burlando@unige.it)

Link to the group or personal webpage:

https://www.gs-windyn.it/

https://rubrica.unige.it/personale/UkNHX15h

References:

o   Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55. https://doi.org/10.1038/nature14956

o   Blocken, B. (2015). Computational fluid dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations. Building and Environment, 91, 219–245. https://doi.org/10.1016/j.buildenv.2015.02.015

o   Giorgi, F. (2006). Climate change hot-spots. Geophysical Research Letters, 33(8). https://doi.org/10.1029/2006GL025734

o   Guida C., C. Gargiulo, R. Papa, and G. Carpentieri (2022). Vulnerability and exposure of Mediterranean coastal cities o climate change-related phenomena. Environ. Sci. Proc. 21(1), 79. DOI: https://doi.org/10.3390/environsciproc2022021079

o   Lagasio M., F. Silvestro, L. Campo, and A. Parodi (2019). Predictive capability of a high-resolution hydrometeorological forecasting framework coupling WRF cycling 3DVAR and continuum. J. Hydrometeor., 20, 1307–1337. DOI: https://doi.org/10.1175/JHM-D-18-0219.1

Research topic G

Title: Geo-Hazard of Turbidity Currents on Underwater Infrastructures in a Changing Climate

Proposers: Michele Bolla Pittaluga

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: The aim of this PhD fellowship is analysing the impact of turbidity currents on underwater infrastructures. Turbidity currents are sediment gravity flows that, once initiated, can traverse considerable distances, spanning hundreds to thousands of kilometres into the deep ocean, and reach velocities as high as 20 m/s (Piper et al., 1999). The potential consequences for infrastructure along their path are severe, as demonstrated by the impact on the submarine cable sector dating back to the installation of the first transatlantic cables, notably during the 1929 Grand Banks earthquake and other subsequent events. Recent cable displacements and ruptures in Taiwan's Gaoping Canyon and in the Congo Canyon serve as a stark reminder of this ongoing risk faced by offshore facilities (Carter et al., 2012; Talling et al., 2022). Amid the rapid expansion of submarine telecommunication and power cables, offshore oil & gas projects, scientific monitoring experiments, deep-sea mining, and renewable energy initiatives since the 1990s, the exposure of seafloor infrastructures to storm-induced turbidity currents becomes an increasingly significant geo-hazard that demands effective management strategies. Coupled with the increased exposure associated with the growth of offshore infrastructure installations trends in climate change indicate that a warming ocean could lead to the development of larger and stronger cyclonic storms (Emanuel, 2003). Understanding the effect of stronger storms, on coastal climate on the frequency and magnitude of turbidity currents is a topic that remains largely unknown.

In response to these challenges, this PhD research project involves a comprehensive and systematic investigation of the complex interactions between storms, triggering and development of deep-sea sediment gravity flows. The project's multi-faceted approach starts with the analysis of synthetic tropical cyclone databases, enabling controlled studies even in regions with limited historical data, such as those at the edges of cyclone basins. This approach combines the benefits of using global climate models and synthetic modelling to generate future climate synthetic tropical typhoons spanning hundreds of years (Bloemendaal et al., 2022). Parametric relations for cyclonic wind and pressure fields will be employed to extract site-specific data from these databases, while refined parametric formulas for cyclone-induced waves and storm surges will provide crucial information on wave and water level conditions across selected study sites. The analytical wind field formulation will be calibrated through the results of high-resolution simulations of typhoons performed in selected areas of interest.

The outcome of this research project will aid in developing targeted mitigation strategies, enhancing the resilience of energy and telecommunication networks, discriminating between natural and anthropogenic risks for underwater infrastructures, and supporting sustainable coastal and offshore development in storm-prone areas, paving the way for sustainable development and climate adaptation in the face of increasing cyclonic activities and sea level rise.

For more information please contact: Michele Bolla Pittaluga, michele.bollapittaluga@unige.it

Link to the group or personal webpage: http://www3.dicca.unige.it/miki/index.html

References:

o   Porcile, G., Bolla Pittaluga, M., Frascati, A., Sequeiros, O.E., 2023. Modelling the air-sea-land interactions responsible for the direct trigger of turbidity currents by tropical cyclones, Applied Ocean Research, 137, 103602, https://doi.org/10.1016/j.apor.2023.103602

o   Porcile, G., Bolla Pittaluga, M., Frascati, A., Sequeiros, O.E., 2020. Typhoon-induced megarips as triggers of turbidity currents offshore tropical river deltas, Commun. Earth Environ. (Nature Publisher Group), 1, 2, 1-13, doi: 10.1038/s43247-020-0002-1

o   O.E. Sequeiros, M. Bolla Pittaluga, A. Frascati, C. Pirmez, D.G. Masson, P. Weaver, A.R. Crosby, G. Lazzaro, G. Botter, J.G. Rimmer, 2019. How typhoons trigger turbidity currents in submarine canyons, Scientific Reports (Nature Publisher Group), 9, 1–15, doi: 10.1038/s41598-019-45615-z

Research topic H

Title: Floor response spectra for the seismic risk assessment of nonstructural components in reinforced concrete buildings

Proposers: Stefania Degli Abbati, Sergio Lagomarsino

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: The seismic assessment of nonstructural components (NSCs)—such as infill walls, architectural elements, machinery, and equipment housed within buildings—is a critical aspect of performance-based earthquake engineering. This is because most of the earthquake-induced damage and related economic losses are often attributed to these components. In many past major earthquakes, damage to NSCs resulted in losses that far exceeded those caused by structural damage. Moreover, such damage can severely affect the performance or functionality of critical facilities, such as hospitals. NSCs are typically classified based on their sensitivity to acceleration, deformation, or both. This PhD proposal focuses on acceleration-sensitive NSCs, for which seismic demands are commonly characterized using floor response spectra (FRS). Numerous formulas have been proposed in the literature and building codes to predict FRS for the seismic assessment of NSCs in ordinary buildings or artistic pieces hosted in monumental structures. However, these formulas are generally calibrated for specific structural typologies, raising doubts about their applicability to other building types. In this context, the proposed research aims to: (i) investigate the key parameters influencing FRS, such as higher vibration modes, structural nonlinearity, and NSC damping; and (ii) validate practitioner-oriented tools currently recommended in the literature and building codes for FRS prediction. To achieve these objectives, the study will use real data from earthquake-affected buildings monitored by the Italian Seismic Monitoring Network, in combination with parametric numerical simulations based on models calibrated using data from dynamic identification tests. The analysis of this measured data, together with numerical results, will support the definition of damage thresholds used to establish operational and strategic limit states, primarily associated with the performance and damage of NSCs.

For more information please contact: Stefania Degli Abbati (stefania.degliabbati@unige.it)

Link to the group or personal webpage:

https://rubrica.unige.it/personale/UkNGXlth

https://rubrica.unige.it/personale/VUZEXlpu

References:

• Degli Abbati S., Cattari S., Lagomarsino S. “Theoretically-based and practice oriented formulations for the floor spectra evaluation”. Earthquakes and Structures 15(5):565-581, 2018.

• Degli Abbati S., Cattari S., Lagomarsino S. “Validation of a practice‐oriented floor spectra formulation through actual data from the 2016/2017 Central Italy earthquake”, Bulletin of Earthquake Engineering 20:7477–7511, https://doi.org/10.1007/s10518-022-01498-6, 2022.

Research topic J

Title: Structural Health Monitoring of existing buildings for the prevention of anthropogenic and environmental risks

Proposers: Marco Lepidi, Serena Cattari, Daniele Sivori

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Existing buildings credited with strategic and monumental value, especially if exposed to serious anthropogenic and environmental risks, require continuous monitoring and reliable assessment of their structural health and safety to extend their service life. This problem often presents challenging tasks due to the building complexity, the material heterogeneities, and the unavailability of documentation. This research proposes a novel framework for Structural Health Monitoring (SHM) that integrates traditional analytical and computational techniques with advanced sensors and intelligent new methodologies to address these challenges.  The first research stage deals with the fusion and treatment of big data collected from a rich cross-section of sample buildings by multi-physical sensor networks, to establish a behavioural baseline of the structural response. Particular attention will be devoted to understand the role of environmental effects thanks to big data of daily monitoring provided by  the Italian Seismic Observatory of Structures on a large set of historical unreinforced masonry buildings. Results will be useful also to more effectively address the monitoring of large portfolios of structures.  The accurate picture emerging from this data-based background is the standing point for the second stage, aimed at solving inverse structural problems. The third stage involve the standardized development of reliable and effective data-based physically-informed models, by balancing the requirements of model synthesis and mechanical representativeness. Finally, the resulting data-based physically-informed models will be employed to simulate the structural patterns that may result from anthropogenic activities (e.g. conservation, restoration, refurbishment interventions), as well as the response to various environmental hazards (extreme natural events). Some key steps of the research path will be assisted by novel Artificial Intelligence tools (e.g. to automate the classification of anomalies in experimental modal analysis).

For more information please contact:

serena.cattari@unige.it (S. Cattari)

marco.lepidi@unige.it (M. Lepidi)

daniele.sivori@unige.it (D. Sivori)

Link to the group or personal webpage:

Webpage (S. Cattari)

Webpage (M. Lepidi)

Webpage (D. Sivori)

References:

o   Sivori, D., Cattari, S., Lepidi, M. (2022). A methodological framework to relate the earthquake-induced frequency reduction to structural damage in masonry buildings. Bulletin of Earthquake Engineering, 20(9), 4603-4638. https://doi.org/10.1007/s10518-022-01345-8

o   Sivori, D., Lepidi, M., Cattari, S. (2020). Ambient vibration tools to validate the rigid diaphragm assumption in the seismic assessment of buildings. Earthquake Engineering & Structural Dynamics, 49(2), 194-211. https://doi.org/10.1002/eqe.3235

o   Sivori, D., Merani, M. G. B., Bocchi, F., Spina, D., Cattari, S. (2025). Environmental effects on the experimental modal parameters of masonry buildings: experiences from the Italian Seismic Observatory of Structures (OSS) network. Journal of Civil Structural Health Monitoring, 15(2), 307-331. https://doi.org/10.1007/s13349-024-00847-0

Research topic K

Title: AI-Driven Meta-Modelling of Bio-Inspired Thermal Energy Storage Harvesters made of Low-Carbon Cementitious for the Built Environment

Proposers: Prof. Antonio Caggiano (DICCA-UniGE), Dr. Juan Hostos (CIMEC-CONICET, Argentina)

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: This PhD project offers an opportunity to explore AI-driven, multiscale modelling and digital twin development for bio-inspired thermal energy storage (TES) systems, made of low-carbon cement composites and embedded phase change materials (PCMs).

This PhD call offers an exploration of AI-driven, multiscale modelling and digital twin development for bio-inspired thermal energy storage (TES) systems using low-carbon cement and embedded PCMs. The candidate is call to meta-design TES units enabling meta heat transfer and structural integrity. 

The work will integrate:

• Topology optimization (SIMP, level-set) under coupled mechanical and enthalpy-based models.

• Multiscale homogenization (i.e., FE² and FFT-based methods) to derive effective thermal-mechanical laws.

• Reduced-order modelling (ROM) and real-time digital twins, embedding sensor feedback to adapt geometry and predict behaviour under cyclic thermal loads.

• Physics-based machine learning (ϕML-TES) or similar for inverse design and manufacturability control, delivering STL/STEP files directly to 3D-printing.

• Key performance outputs can include i) energy harvesting/efficiency parameters, ii) TESₘₑₜₐ-parameters, iii) σₘₐₓ/σₐₗₗₒwed stress-level, iv) CO₂-eq saving via LCA tools.

This work offers cross-disciplinary training in advanced numerical methods, AI, optimization, materials science, and sustainable design.

A key aspect of the project is the collaborative opportunity for PhD candidates, which includes up to 18 months of mobility abroad through partnerships with international institutions. The call is also open for a Double PhD program, allowing candidates to earn degrees from both UniGE and partner universities.

For more information please contact: Antonio Caggiano antonio.caggiano@unige.it

Link to the group or personal webpage:

References:

o   Caggiano, A., Peralta, I., Fachinotti, V. D., Goracci, G., & Dolado, J. S. (2023). Atomistic simulations, meso-scale analyses and experimental validation of thermal properties in ordinary Portland cement and geopolymer pastes.

o   Fachinotti, V. D., Peralta, I., Toro, S., Storti, B. A., & Caggiano, A. (2023). Automatic generation of high-fidelity representative volume elements and computational homogenization for the determination of thermal conductivity in foamed concretes. Materials and Structures, 56(10), 179.

o   Fachinotti, V. D., Álvarez-Hostos, J. C., Peralta, I., Zanjani, M. K., Berardi, U., Pisello, A. L., ... & Caggiano, A. (2024). Reviewing numerical studies on latent thermal energy storage in cementitious composites: report of the RILEM TC 299-TES. Materials and Structures, 57(10), 247.

o   Hostos, J. C. Á., Storti, B., Lefevre, N., Sobotka, V., Le Corre, S., & Fachinotti, V. D. (2023). Design via topology optimisation and experimental assessment of thermal metadevices for conductive heat flux shielding in transient regime. International Journal of Heat and Mass Transfer, 212, 124238. 

Research topic L

Title: Data-driven NaTech Risk Assessment under Climate Crisis conditions

Proposers: Bruno Fabiano

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Background and Motivation

Natural Hazard Triggering Technological Disasters (NaTech) represent a growing threat due to the increasing frequency and severity of extreme weather events driven by climate change. These cascading events, such as floods triggering chemical leaks or wildfires impacting energy infrastructure, pose complex and dynamic challenges to risk assessment frameworks not designed for evolving hazard landscapes, also in connection with energy transition. Traditional risk models often rely on static historical data, limiting their effectiveness in anticipating and managing real-time NaTech scenarios.

Research Aim

This PhD project aims to develop a novel framework for dynamic NaTech risk assessment that integrates real-time environmental monitoring with advanced Machine Learning (ML) algorithms. The goal is to enhance the predictive capability and responsiveness of current risk assessment systems by combining data-driven approaches with domain expertise in environmental and industrial safety.

Objectives

Background: critical assessment of the state-of-the-art, bibliometric analysis and research gap identification.

Data Acquisition and Integration: Collect and harmonize multi-source real-time data, including satellite imagery, sensor networks, climate forecasts, and industrial safety reports.

Feature Extraction and Event Detection: Develop ML pipelines to detect early signals of NaTech risks from unstructured and high-frequency data streams.

Dynamic Risk Modeling: Design and validate ML-driven models (e.g., LSTM networks, spatiotemporal Bayesian models) to continuously update NaTech risk profiles in response to modification induced by energy transitions and environmental conditions.

Prototype Development: Build a real-time decision support tool for emergency managers and industrial operators, incorporating explainable AI techniques for transparency and trust.

Expected Contributions

The research will contribute to a shift from static to adaptive NaTech risk assessment methodologies. It will provide a robust, scalable, and explainable ML-based framework that improves situational awareness and response times. The work supports climate adaptation strategies by enabling proactive risk mitigation in critical infrastructure systems.

Supervision and Collaborations

The project will be developed in collaboration with Regional environmental risk management agencies, industrial safety bodies, and academic partners expert in machine learning and complex systems.

For more information please contact: Bruno Fabiano – e-mail: brown@unige.it

Link to the group or personal webpage: https://dicca.unige.it/laboratori/lab_chimica/sicurezza_industriale_ambientale

References:

o   Energy transition technology comes with new process safety challenges and risks. Pasman, H., Sripaul, E., Khan, F. Fabiano, B. Process Safety and Environmental Protection, 2023, 177, pp. 765–794.

o   Methodology for probabilistic tsunami-triggered oil spill fire hazard assessment based on Natech cascading disaster modelling. Nishino, T. , Miyashita, T. , Mori, N. Reliability Engineering and System Safety, 242, 109789 2024

o   Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework. Xiaolong Luo, Ana Maria Cruz, Dimitrios Tzioutzios,  Int J Disaster Risk Sci (2020) 11:735–750

Research topic M

Title: Role of the accuracy of precipitation measurements and spatial representation of the urban environment in the assessment of flood risk

Proposers: Arianna Cauteruccio, Luca G. Lanza

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: Flood risk assessment and mitigation in urban areas relies on the accurate quantification of the flood hazard, deriving from the knowledge and understanding of the space-time evolution of intense precipitation events. Moreover, the spatial description of the urban environment provided in digital form (e.g. Digital Surface Model – DSM or Digital Elevation Model – DEM) by authoritative institutions is usually too coarse, therefore, unsuitable to represent the microtopography features of the urban environment and to model the small variations of flood propagation.

Traditional catching-type instruments measure the integral properties of rainfall (i.e., rainfall intensity and cumulative rainfall), while more recently developed non-catching instruments (including disdrometers) can also measure the microphysical properties of rainfall including the drop size distribution (DSD). The research focuses on the propagation of the biases of disdrometer measurements with respect to wind effects and instrumental factors into the assessment of flood risk scenarios. Results will allow improving statistical analysis of rainfall time series at climatological scales, investigating joint rain/wind climatology features at selected test sites, deriving extreme rainfall event statistics and trends aimed at enhancing the quantification of the risk of flooding in the urban environment. The research addresses also the role of the microtopography features that characterize the urban environment in flood propagation modelling and vulnerability assessment of the exposed assets, by exploiting the possibility to detect stairs, walls, footpaths and other small obstacles by analysing aerial images or georeferenced available databases.

For more information please contact:

Arianna Cauteruccio, arianna.cauteruccio@edu.unige.it

Luca G. Lanza, luca.lanza@unige.it

Link to the group or personal webpage:

Arianna Cauteruccio, https://www.researchgate.net/profile/Arianna-Cauteruccio

Luca G. Lanza, https://www.researchgate.net/profile/Luca-Lanza

References:

o   Cauteruccio, A., Arata, L., Parodi, M., Chinchella, E., & Lanza, L. G. (2025). Laboratory testing and modelling of the hydrological performance of a resin gravel permeable pavement. Hydrological Sciences Journal, 70(6), 898–908. https://doi.org/10.1080/02626667.2025.2461696

o   Loglisci, N., Boni, G., Cauteruccio, A., Faccini, F., Milelli, M., Paliaga, G., and Parodi, A. (2024). The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy, Nat. Hazards Earth Syst. Sci., 24, 2495–2510, https://doi.org/10.5194/nhess-24-2495-2024

o   Cauteruccio, A., Chinchella, E., and Lanza, L. G. (2024). The overall collection efficiency of catching type precipitation gauges in windy conditions. Water Resour. Res., 60 (1), e2023WR035098. https://doi.org/10.1029/2023WR035098

o   Chinchella E., Cauteruccio A. and L.G. Lanza (2024). The impact of wind on precipitation measurements from a compact piezoelectric sensor, J. of Hydromet., 25 (2), 339 – 352. https://doi.org/10.1175/JHM-D-23-0180.1

o   Chinchella E., Cauteruccio, A., & Lanza, L. G. (2024). Quantifying the windinduced bias of rainfall measurements for the Thies CLIMA optical disdrometer. Water Resources Research, 60 (10), e2024WR037366. https://doi.org/10.1029/2024WR037366    

o   Cauteruccio, A., Brambilla, E., Stagnaro, M., Lanza, L. G., & Rocchi, D. (2021). Wind tunnel validation of a particle tracking model to evaluate the wind-induced bias of precipitation measurements. Water Resources Research, 57(7), e2020WR028766. https://doi.org/10.1029/2020WR028766

o   M. Colli, M. Stagnaro, A. Caridi, L.G. Lanza, A. Randazzo, M. Pastorino, D.D. Caviglia and A. Delucchi (2019). A field assessment of a rain estimation system based on earth-to-satellite microwave links. IEEE Transactions on Geoscience And Remote Sensing, 57(5), 2864-2875, https://doi.org/10.1109/TGRS.2018.2878338

o   Palla, A., Colli, M., Candela, A., Aronica, G.T. and L.G. Lanza (2018). Pluvial flooding in urban areas: the role of the surface drainage efficiency. J. Flood Risk Management, 11, S663-S676, https://doi.org/10.1111/jfr3.12246

Research topic N

Title: Structural response of bell towers to environmental changes

Proposers: Chiara Calderini, Stefano Podestà

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: The structural response of predominantly vertical structures (civic towers or bell towers) is conditioned by multiple factors, in which variations in environmental parameters play a significant, albeit often underestimated, role. In this context, the monitoring data available on numerous bell towers show how displacements and consequently the state of stress is influenced in a non-negligible manner by thermal variations induced by solar radiation. The different levels of insolation that can be affected on the walls of the same structure, characterized by a prevalent vertical development determines deformations and consequently, depending on the thermal expansion coefficient, different stress states between the different walls, which can interpret the cyclic and seasonal movement of multiple bell towers. The project, starting from the study of the monitoring data of the Garisenda Tower and the Asinelli Tower in Bologna (Italy), aims to understand and interpret how the thermal variation detectable daily and seasonally on the two towers conditions the global displacements of the two artefacts.

For more information please contact:

Chiara Calderini, chiara.calderini@unige.it

Stefano Podestà, stefano.podesta@unige.it

Link to the group or personal webpage:

References:

o   Curti, E., Podestà, S., Scandolo, L. (2012). Simplified mechanical model for the seismic vulnerability evaluation of belfries. International Journal of Architectural Heritage, Volume 6, Issue 6, pp. 605 – 625, November 2012, doi: 10.1080/15583058.2011.594932.

o   Ramirez, R., Ghiassi, B., Pineda, P., Lourenço, P.B. (2025). Moisture and Temperature Effects on Masonry Structures: The Civic Tower of Pavia as a Case Study. Lecture Notes in Civil Engineering, Volume 613 LNCE, Pages 845 – 865, 18th International Brick and Block Masonry Conference, IB2MaC 2024, Birmingham, 21 July 2024 through 24, doi: 10.1007/978-3-031-73314-7_65