Science

Call for applications are open.

Deadline: 29 May 2026, 12:00 p.m. (italian time)

Excitonic Quantum States as Multi-Dimensional Observables for Molecular Sensing in 2D Materials

Background and motivation

Excitons—bound electron–hole pairs—are inherently quantum objects whose population, coherence, and symmetry properties are highly sensitive to their environment. This project proposes to establish excitonic states in two-dimensional materials as high-dimensional sensing observables for molecular adsorption. Moving beyond conventional chemiresistive detection, we will investigate whether gas-phase molecules (NH₃, NO₂, H₂S, and small chiral molecules) can perturb or quench excitonic states in layered semiconductors, including transition metal dichalcogenides (MoS₂, WS₂, WSe₂), graphene-based heterostructures, and magnetic halides (e.g. CrBr₃, CrCl₃). The central hypothesis is that adsorption-induced charge transfer, dielectric screening, and defect activation modify exciton lifetimes, binding energies, linewidths, and polarization signatures in analyte-specific ways. Using in situ, time-resolved photoluminescence and pump–probe spectroscopy under controlled ppm gas dosing, combined with simultaneous transport measurements, we will quantify adsorption–exciton coupling mechanisms and extract kinetic parameters from optical transients. TMD monolayers serve as robust, room-temperature excitonic systems, enabling systematic mapping of quenching and spectral renormalization. Graphene–TMD hybrids will allow optical excitonic perturbations to be transduced into electrical signals. Magnetic chromium trihalides provide a unique platform where excitons are coupled to lattice and magnetic order, potentially amplifying adsorption sensitivity.

The project will deliver a mechanistic atlas linking molecular adsorption to excitonic quantum observables and demonstrate a proof-of-concept excitonic sensing device. By reframing adsorption-induced exciton perturbation as a quantum-enabled sensing modality, the work bridges condensed matter physics, surface chemistry, and quantum technologies.

This joint project between Università Cattolica del Sacro Cuore (UCSC) and KU Leuven labs requires a PhD student with a solid background in experimental materials science, surface chemistry, or applied physics. During the project, skills in time-resolved optical spectroscopies and transport measurements will be developed, along with application-driven unconventional gas sensing methods, chiral molecular recognition, and data analysis based on machine-learning techniques.

Candidate Profile

• Master's degree or comparable qualification in Physics, Chemistry, Materials Science and Engineering or related fields. The title must be obtained before October 31st, 2026;
• A strong interest for m---ultidisciplinary research is required;
• Previous experience in the topics of the project are welcome but not required;
• Candidates should have a solid background in the field of the project;
• Good knowledge of the English language, both spoken and written, is essential;
• Strong commitment, ability to work in a team, and eagerness for international mobility is desired;

Opportunities

• Experimental research participating to the international collaboration between Università Cattolica del Sacro Cuore (UCSC) and KU Leuven with at least one year spent in both institutions;
• Double degree opportunity - PhD in Science from Università Cattolica del Sacro Cuore (UCSC) and PhD in Chemisty from KU Leuven.

Supervisors

Prof. Luigi Sangaletti, Università Cattolica del Sacro Cuore, Italy, luigi.sangaletti@unicatt.it
Prof. Steven De Feyter, KU Leuven, Belgium, Department of Chemistry, steven.defeyter@kuleuven.be

Pricing Models Integrating Behavioral Components, Nonlinear Dynamics, and Probabilistic Machine Learning

Background and motivation

General-equilibrium asset pricing models have long provided a foundational framework for understanding risk and return in financial markets. However, these approaches typically rely on simplifying assumptions, such as rational expectations, linear dependencies, and stable distributions of asset returns. Empirical evidence accumulated over the past decades has revealed systematic deviations from these assumptions, including behavioral biases, regime shifts, volatility clustering, and heavy-tailed return distributions.

At the same time, advances in the study of nonlinear systems and complex networks have emphasized the role of feedback loops and endogenous instability in financial markets. In parallel, recent developments in probabilistic machine learning have opened new opportunities for modeling financial systems under uncertainty.

In light of these developments, the first task of the PhD student will be to develop next-generation asset pricing models and risk management frameworks by integrating insights from behavioral finance, nonlinear market dynamics, and advanced probabilistic machine learning techniques. The second task is to explore quantum-inspired and game-theoretic extensions of classical portfolio theory, offering innovative approaches to portfolio optimization under uncertainty.

In particular, the research will focus on quantum annealing and gate-based algorithms, which may offer advantages in exploring large combinatorial search spaces, as well as on hybrid classical–quantum frameworks that may enable more efficient solutions to complex financial optimization problems.

Candidate Profile

• Master's degree or comparable qualification in Finance, Mathematics, Mathematical Engineering or related fields. The title must be obtained before October 31st, 2026;
• A strong interest for multidisciplinary research is required;
• Previous experience in the topics of the project are welcome but not required;
• Candidates should have a solid background in the field of the project;
• Good knowledge of the English language, both spoken and written, is essential;
• Strong commitment, ability to work in a team, and eagerness for international mobility is desired.

Opportunities

• Experimental research participating to the international collaboration between Università Cattolica del Sacro Cuore (UCSC) and KU Leuven with at least one year spent in both institutions;
• Double degree opportunity - PhD in Science from Università Cattolica del Sacro Cuore (UCSC) and PhD in Mathematics from KU Leuven.

Supervisors

Prof. Davide Radi, Università Cattolica del Sacro Cuore, davide.radi@unicatt.it
Prof. Wim Schoutens, KU Leuven, Belgium, wim.schoutens@kuleuven.be

New insights into N2O emissions from terrestrial ecosystems

Background and motivation

Nitrous oxide (N₂O) is a greenhouse gas of significant climatic relevance and an agent of stratospheric ozone depletion, with a global warming potential approximately 300 times that of CO₂ over a 100-year period (IPCC AR6, 2023). Atmospheric N₂O concentrations have continued to rise over recent decades, driven by both anthropogenic activities and biogenic emissions from natural and semi-natural soils (Thoning et al., 2022). While nitrogen loads seem to drive N₂O emission rates in grasslands (Zhang et al., 2021), recent evidence suggests that in temperate forests, environmental conditions may limit N₂O fluxes more significantly than nitrogen availability itself (Toteva et al., 2025; Yu et al., 2022).

Despite their importance, temperate forests have been less extensively studied for their N₂O fluxes compared to agricultural systems, and substantial uncertainties remain in the global budget of this gas (Christensen, 2024; Reay & Davidson, 2012).

Direct measurements of ecosystem-scale vertical N₂O fluxes using the eddy covariance (EC) technique are still relatively scarce. However, recent advances in high-frequency instrumentation and signal processing of low-intensity emissions (Krebs et al., 2024) are opening new possibilities for continuous monitoring. For example, recent EC studies in forested ecosystems have shown that N₂O can significantly affect the overall greenhouse gas balance even in systems where CO₂ exchange dominates. This highlights the urgent need to integrate surface models and machine learning approaches to fill data gaps and disentangle the complex temporal and spatial drivers of these fluxes (Tikkasalo et al., 2025).

While EC provides an integrated ecosystem-scale view, soil-scale N₂O emissions are traditionally measured using soil enclosures (soil chambers), although reproducibility and scalability of these measurements are still questionable (Triches, 2025).

Despite some advances, substantial gaps remain in understanding:

• the integration of ecosystem-scale (eddy covariance) measurements with soil-scale (chamber) measurements;
• the partition of soil versus canopy contributions to the total ecosystem N₂O exchange;
• the flux variability as a function of forest microclimates and soil conditions;
• the applicability of high-frequency instrumentation specifically calibrated for N₂O in temperate forests.

Finally, recent attempts to develop process-based models for predicting N₂O emissions from global land ecosystems are shedding light on the environmental factors that drive these emissions (Ma et al., 2025) and suggesting possible mitigation and control actions.

Aims

The primary objective of this PhD project is to characterize the magnitude, temporal and spatial variability, and principal environmental drivers of vertical N₂O fluxes in a temperate forest and in grasslands. By focusing on two different terrestrial ecosystems, the project aims to quantify the relative contributions of soil, understory, and canopy surfaces to the overall ecosystem-level budget of these greenhouse gases.

This will be done by performing a multi-year measurement campaign in a temperate forest of the Po Valley and in one or more grassland sites, combining:

• Eddy covariance measurements with a middle-infrared laser absorption spectrometer for high-frequency N₂O flux;
• Automated dynamic chambers on soil and selected microenvironments to resolve soil and surface-type contributions to the N₂O flux.

The measurements will be carried out primarily at the ICOS It-BFt ecosystem station, which is specially equipped for long-term flux measurements and concentration profiles, but also at other grassland sites to be identified.

References

• Christensen, S. (2024). Global N₂O emissions from our planet: Which fluxes. Environmental Health Sciences, 14(0015.1);
• IPCC (2023). Climate Change 2023: The Physical Science Basis. Cambridge University Press;
• Krebs, M., Eugster, W., Zeeman, M., & Emmenegger, L. (2024). Atmospheric Measurement Techniques, 17, 1–18;
• Ma, J., Arneth, A., Smith, B., et al. (2025). Geoscientific Model Development, 18, 3131–3155;
• Reay, D. S., & Davidson, E. A. (2012). Trends in Ecology & Evolution, 27(6), 358–366;
• Thoning, K. W., Crotwell, A. M., & Dlugokencky, E. J. (2022). Earth System Science Data, 14, 123–142;
• Tikkasalo, S., Mammarella, I., Rannik, Ü., & Vesala, T. (2025). Biogeosciences, 22, 1277–1295;
• Toteva, G. Y., et al. (2025). Biogeosciences, 22, 7829–7844;
• Triches, N. Y., et al. (2025). Atmospheric Measurement Techniques;
• Yu, L., Li, X., et al. (2022). Frontiers in Soil Science;
• Zhang, X., Davidson, E. A., Smith, P., et al. (2021). Catena, 199, 105105.

Candidate Profile

• Master's degree or comparable qualification in Physics, Environmental Sciences, or adjacent fields. The title must be obtained before October 31st, 2026;
• A strong interest in multidisciplinary research is required;
• Candidates should have a solid background in computer science, physics, micrometeorology and ecology. Skills in instrumental setup, data analysis and programming are also required;
• Good knowledge of the English language, both spoken and written, is essential;
• Strong commitment, ability to work in a team, and eagerness for international mobility is desired.

Opportunities

• Experimental research participating in the international collaboration between Università Cattolica del Sacro Cuore (UCSC) and Universidad de Navarra (Pamplona) with at least one year spent in both institutions;
• Double degree opportunity.

Supervisors

Prof. Giacomo Alessandro Gerosa, Università Cattolica del Sacro Cuore, Italy, giacomo.gerosa@unicatt.it
Prof. David Elustondo, Universidad de Navarra, Spain, delusto@unav.es