Description of 42nd cycle scholarships (2026-2029)

Description: This project develops LLM-based agents to integrate heterogeneous models and data for coastal risk assessment, assisting in model setup, guiding result analysis, and building multi-hazard decision-support systems for rapid, contextualized coastal risk assessments. The framework translates complex scientific data into actionable strategies, enhancing the resilience of coastal communities.
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Contact: Lorenzo Mentaschi (lorenzo.mentaschi@unibo.it)

Description: This research quantifies marine soundscapes by modeling the complex interplay between natural and anthropogenic acoustic sources across space and time. Through scenario-based analysis, the project defines underwater noise dynamics to establish robust monitoring methodologies and mitigation frameworks. Seismo-acoustic noise recorded by buoys or onshore stations will be correlated with oceanographic parameters to develop new monitoring indicators. The findings provide the technical foundation for enhanced environmental impact assessments and the sustainable management of marine environments.
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Contact: Paolo Oddo (paolo.oddo@unibo.it)

Contact: Silvana Di Sabatino (silvana.disabatino@unibo.it)

Description: We are offering a PhD position focused on advancing data assimilation methods for generative diffusion models applied to climate system sciences (atmosphere, ocean, sea ice). The project will investigate and develop ensemble-based, variational, and fully Bayesian approaches to integrate observational data with high-dimensional generative models. The successful candidate will explore how diffusion models can enhance state estimation, uncertainty quantification, and forecasting in complex geophysical systems. The research will combine machine learning, statistical inference, and physical modeling, with applications to climate and environmental prediction. Candidates should have a strong background in applied mathematics, physics, statistics, or a related field, along with experience in numerical methods and/or machine learning. Familiarity with data assimilation or geophysical fluid dynamics is desirable but not required.
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Contact: Alberto Carrassi (alberto.carrassi@gmail.com) / Giovanni De Cillis (giovanni.decillis@unibo.it)

The observed North Atlantic Oscillation (NAO) exhibits pronounced variability in a frequency range corresponding to periods of 7–9 years. Modelling studies have indicated that such variability may arise from ocean–atmosphere interactions and feedbacks involving different components of the ocean circulation in the North Atlantic, including a buoyancy-driven meridional overturning cell and the wind-driven gyres. In addition, the NAO has been shown to exhibit significant decadal predictability in large-ensemble, retrospective forecasts initialized from a realistic state of the climate system, particularly regarding the North Atlantic Ocean.

This PhD will aim to advance the understanding of the physical processes that underpin NAO variability and predictability through the use of observational data, state-of-the-art climate predictions, and tailored-to-purpose modelling experiments. The PhD student will acquire essential computing and analytical skills and will learn to work collaboratively in a research group tackling similar problems as well as independently, leading but also contributing to parallel analyses. Through participation in international workshops and multi-national project meetings will also acquire fundamental soft skills, such as effective scientific communication and ability to interact with other scientists.

The successful applicant should have a solid background in physical sciences and preferably some acquaintance with shell scripting and programming in Python. Prior MSc related to climate sciences (ocean & atmosphere dynamics) would be considered a plus. Fluent use (reading, writing and speaking) of the English language is essential.
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Contact: Panos Athanasiadis (panos.athanasiadis@cmcc.it)

This PhD project aims to quantify the impact of oceanic temperature extremes on terrestrial weather events, including droughts, storms, and land temperature extremes, with a particular focus on the risk of their co‑occurrence and the underlying physical processes. Despite the substantial risks posed by such compound ocean–land events, they remain poorly studied. By comparing the occurrence of ocean–land extremes in state‑of‑the‑art coupled climate simulations and reanalysis datasets spanning the past four decades, the project will assess the contribution of climate change to the intensification of these risks in tropical and subtropical areas. Analysis of future projections will further quantify the evolving nature of concurrent ocean–land hazards.
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Contact: Annalisa Bracco (annalisa.bracco@cmcc.it)