1 Title: Geophysics of the solid Earth
Description: Description: The topic is not fixed a priori and deals with all aspects of solid-Earth geophysics, such as seismology, physics of volcanism, geodesy, geodynamics, tsunami, ...It will be adapted based on the profile of the selected candidate. All profiles in solid-Earth geophysics are welcome, and particularly those based on seismology, volcanology and related modelling and hazards.
Contact: Giorgio Spada (giorgio.spada@unibo.it)
2 . AI-powered hazard mapping
AI-powered hazard mapping is a rapidly evolving field that harnesses machine learning, computer vision, and geospatial analysis to identify, predict, and mitigate a broad range of environmental and anthropogenic risks. This work focuses on leveraging satellite and Earth observation data to monitor dynamic coastal processes and support the mapping of critical hazard scenarios in complex environments
Contact: lorenzo.mentaschi@unibo.it
3. Heat extremes and air pollution: drivers, interactions and impacts
Description: This doctoral research aims to explore the intricate interactions and factors that contribute to the occurrence of combined heat extremes and severe air pollution episodes, as well as their effects on public health.
Contact: erika.brattich@unibo.it
4. Integration of monitoring and modelling techniques for climate adaptation applications
This PhD fellowship will investigate the integration of advanced environmental monitoring systems with numerical and statistical modeling approaches to improve evidence-based climate adaptation strategies and interventions, especially nature-based, in specific contexts of Sub-Saharan Africa. The research is embedded within ALBATROSS’s seven Climate-Resilient Development (CRD) Hubs across Sub‑Saharan Africa—Ghana’s Keta Basin and Kumasi, Tanzania’s Kigamboni, Kenya’s Turkana County, Madagascar’s Morondava and Tamatave, and South Africa’s Umzimvubu Catchment [ albatross-project.eu]. Key objectives include: 1) Exploiting existing datasets of global and regional climate projections to force statistical and dynamical sectoral models, along with national level finer resolution climate projections and observations for validation and bias correction; 2) Fusing monitoring outputs with predictive modelling to quantify the effectiveness of nature-based adaptation solutions—such as agroforestry in Tamatava, coastal erosion buffers in Morondava, or water retention schemes in Turkana—under current and future climate conditions . Through the development of these integrated monitoring-modelling approaches, the fellowship will contribute to Albatross project’s objective of delivering novel climate services for the design and management of effective, eco-system based, climate adaptation strategies. The successful candidate will cooperate with several international research teams, within the framework of the Horizon Europe research and innovation project ALBATROSS (grant agreement No 101137895). More information on ALBATROSS project here: https://albatross-project.eu/
Contact: laurasandra.leo@unibo.it
5.Innovative approaches for sub-seasonal, seasonal and decadal predictions
Description: the candidate will conduct research on innovative methodologies to forecast weather and climate at timescales going from weeks to years. Emphasis will be on identifying successful strategies to combine GCM- and AI-based predictions into hybrid forecasting tools.
Contact: paolo.ruggieri2@unibo.it
6. Sviluppo di operatori osservazione satellitare basati sull’apprendimento automatico e loro integrazione nell’assimilazione dati in modelli meteorologici e climatici
Nowadays AI-, and more specifically machine learning - based, solutions have reached astonishing efficiency and accuracy.
In this project the student will study AI-based emulations of physics-based radiative transfer models used to extract physically relevant atmospheric quantities from radiances measured by onboard satellite sensors.
In a first phase focus will be given to the task of emulating existing state-of-the-art physical models for sake of enhancing computational efficiency.
In the second phase attempts will be done to use AI to learn "new physics” with the aim of improving accuracy on top of efficiency.
Contact: alberto.carrassi@unibo.it
7. Downscaling methods for urban-based climate predictions and projections
Description: the candidate will develop novel downscaling methods for climate simulations on multiple scales with the aim to obtain reliable simulations up to the urban scale.
Contact: silvana.disabatino@unibo.it
8. Resilience potential and adaptive capacity along the Emilia-Romagna coast under climate change impacts
Description: Due to their marked vulnerability, coastal regions are required to adopt sustainable adaptive strategies enhancing their coastal resilience potential against future climatic and environmental scenarios. This project focuses on the Emilia Romagna (ER) coast that, due to natural and anthropogenic factors, is markedly exposed to flooding, erosion and reduction of natural habitats. This calls for a holistic approach, coupling coastal morphological characteristics and evolutionary trends with socioeconomic aspects in management strategies and planning frameworks. For selected sites of the ER coast, based on territorial data and climate-related indicators, and taking into account existing adaptation actions/plans at the regional/local scale, the research will explore new possible integrated co-design approaches and planning scenarios reconnecting the urban and the natural environment in a climate-responsive adaptation strategy.
Contact: Claudia Romagnoli(claudia.romagnoli@unibo.it)
9. Environmental data in descriptive and predictive models of vector-borne diseases
Description: Weather events and climate patterns are known to impact vector populations and the distribution and occurrence of diseases. Over the last two years, the Emilia Romagna Region (North East Italy) witnessed extreme weather events that unveiled the environmental vulnerability with increased incidence of vector borne diseases and febrile neurologic syndromes. At present, no climatic, geographic, and environmental data are systematically collected to understanding, predicting, and mitigating the spread of vector-borne diseases in the study area. This project focuses on the development of forecasting models embedding environmental and climatic data for predicting changes in vectors and vector-borne disease distribution for optimizing disease outbreak preparedness and response in support of the traditional One Health surveillance in place.
Contact: alessandra.scagliarini@unibo.it
10. Advancing Soil Hydrology: Understanding and modeling water processes in an Earth System Model
Description: This PhD opportunity invites aspiring researchers to explore the complexities of soil hydrology, with a focus on soil and vegetation water-driven processes and the role of soil in global water and carbon cycles. Candidates will investigate how soil properties interact with environmental factors to influence hydrological processes, such as infiltration, evaporation, and runoff. Through cutting-edge modeling, this project aims to enhance our understanding of soil-water, vegetation-water, and soil-river-ocean relationships, contributing to sustainable land management practices, climate resilience, and water resource management. The successful candidate will work on developing novel approaches for assessing and improving soil water representation in state-of-the-art Earth System Model.
This scholarship is co-founded by CMCC
Contact: Dr. Silvio Gualdi silvio.gualdi@cmcc.it
11. Modelling the land surface - atmosphere interaction for a better understanding of climate change
Description: This PhD project aims to advance our understanding of climate change by improving the representation of land surface–atmosphere interactions in Earth system models. The research will focus on key processes such as evapotranspiration, surface energy fluxes, and soil–vegetation feedback, assessing how changes in land use and vegetation dynamics influence local and global climate patterns. By integrating observational data with high-resolution modelling techniques, the project seeks to reduce uncertainties in climate projections and support the development of more robust adaptation and mitigation strategies.
This scholarship is co-founded by CMCC
Contact: Dr. Silvio Gualdi silvio.gualdi@cmcc.it
12. Petrophysical tomography of Campi Flegrei caldera
Description: Earth Sciences rely on computational modelling to understand and predict the evolution of complex physical systems. For magmatic systems, the obvious problem of scale is coupled with heterogeneity that can only be solved with new-generation supercomputers. Over the last decade, several high-performance solvers have been developed to connect the thermodynamics of magmatic systems with the corresponding physical responses. The substantial difference lies in the speed of forward modelling - from seconds for a single simulation with the older-generation software to milliseconds for hundreds of parallelized simulations. This speed has enabled the development of an innovative technique for transdimensional Bayesian inversion. From dispersion curves obtained through noise measurements, the technique enables the determination of petrophysical parameters, including temperature, geochemical differentiation, damage, and melt percentage, in 3D space.
The PhD candidate will utilize information derived from experimental studies of magma rheology and link it to the geophysical velocity responses at Campi Flegrei, leveraging the latest developments in computational thermodynamics and geophysical inversion problems. Reviewing the results obtained from previous seismic campaigns, the candidate will incorporate specific petrophysical and geochemical relations for Campi Flegrei into the forward model, which is necessary to ensure consistency with the geophysical velocity, as well as attenuation and electromagnetic responses. This work will support the modelling activities within the SAKURA research line, part of the Earth Telescope initiative, by developing internal knowledge at INGV on an innovative technique that promises to revolutionize volcanic tomography.
Contact: Luca De Siena (luca.desiena2@unibo.it)
13. Improving Regional Ocean Short-Term Forecasting: Optimizations and Innovations
Description: Improvements in short-term marine forecasts represent a crucial challenge for numerous sectors, including navigation, fishing, tourism, marine resource management, and the prediction of extreme events. It is essential to develop accurate forecasts of marine conditions to ensure the safety and efficiency of operations at sea. In recent years, significant progress has been made thanks to optimizations and technological innovations that have revolutionized the field of weather-marine forecasting. One of the main optimizations concerns the use of machine learning algorithms that can enhance the models' ability to deliver accurate ocean forecasting data, predict extreme events and provide sufficient warning to adopt effective preventive measures. Another methodology to achieve more accurate and reliable forecasts is based on ensemble forecasting, an approach that combines multiple simulations to produce a set of forecasts that provide an assessment of uncertainty. This method, based on performing numerous parallel forecasts with perturbations in initial conditions, boundary conditions, or model parameters, allows for a probabilistic representation of future events, improving the accuracy of forecasts and the management of risks associated with marine conditions.
This scholarship is founded by CMCC
Contact: Dr. Emanuela Clementi emanuela.clementi@cmcc.it
14. Marine pollution modelling and risk mapping
Description: Oceanographic monitoring and modeling are widely used to study the pathways and fate of marine pollutants, such as hydrocarbons and marine litter, in the ocean environment. The fellowship will further develop pollutant models (e.g., the MEDSLIK-II oil spill model) and integrate them with AI algorithms to improve their actionable use in operational applications. Moreover, the candidate will leverage Earth Observation (EO) techniques to trace pollutants at local, regional, and global scales. Numerical modeling and AI tools will be employed to enhance predictions and map risks in coastal areas. Key questions addressed by this fellowship may include: Which factors affect the dispersion of pollutants in the marine environment? What happens to contaminants on the ocean’s surface and in the water column? How do marine pollutants interact with marine habitats? The results of the fellowship will ultimately support scientific and societal challenges related to the Sustainable Development Goals (e.g., SDG 14) and the CoastPredict United Nations Ocean Decade Programme.
This scholarship is founded by CMCC
Contact: Dr. Giovanni Coppini giovanni.coppini@cmcc.it
15. Next-Generation AI-based multi-hazard framework for coastal resilience
Description: Coastal hazards, including extreme waves, storm surges, marine heatwaves, and coastal erosion, pose significant risks to maritime communities and ecosystems. Understanding and predicting these hazards require advanced ocean modeling techniques.This study aims to enhance coupled ocean models by incorporating new processes to better represent physical dynamics at the coastal scale. AI techniques will enhance processes that are not explicitly solved or parametrized in numerical models, particularly in very nearshore dynamics. Calibration and validation will be crucial aspects of the study, involving a comparison of numerical solutions against available observations, exploiting new EO data. The outcome of the project aims to develop a relocatable hazard mapping system integrating coupled deterministic modeling and AI-based emulators to support the development of a decision support system for forecasting and policymaking.
This scholarship is founded by CMCC
Contact: Dr. Salvatore Causio salvatore.causio@cmcc.it
16. Advance the numerical modeling of land-sea water and coastal processes
Description: Catchment hydrology and marine hydrodynamics processes are strongly interconnected at all the time and spatial scales. The new generation numerical modeling is expected to provide a comprehensive representation of the coastal water cycle through seamless and /or coupled approaches. These methods enable a more accurate representation of multi-physics processes occurring at the land-sea interface, such as compound flooding, droughts, and inland water salinization. The ambition of the PhD Project is to provide a seamless modeling of the inland and marine water over a catchment-sea continuum selected within the Mediterranean basin by using a high-resolution finite element modelling on an unstructured-grid. The study will seek to enhance the capabilities of a numerical model conceived to solve the hydrodynamics of the coastal sea water. The well-consolidated wet-and-dry approach will be advanced to account for coastal, pluvial and fluvial flooding as well as drought conditions. Additionally, the accuracy of numerical schemes and parameterizations will be evaluated to improve the model’s ability to handle multiscale processes. A key innovation will be incorporating infiltration processes to refine the water budget representation. Overall the PhD project will contribute to untangle the attribution of land-sea coastal process by distinguishing the role of multiple drivers and their non-linear interaction.
This scholarship is founded by CMCC
Contact: Dr. Giorgia Verri giorgia.verri@cmcc.it