Mobility experience with a research focus
PhD sandwich, Post Doc
Department of Earth Sciences "Ardito Desio"Master degree in Geophysics, University of Milano
This project aims to advance the interpretation of tower-based measurements of turbulence and scalar fluxes over forests by explicitly accounting for horizontal heterogeneity induced by topography. By combining high-resolution Large Eddy Simulations (LES) with observations, the project will provide a physically consistent framework to contextualize measurements that are traditionally analyzed under assumptions of horizontal homogeneity.
The focus is on forested environments where even gentle terrain variations such as valleys and slopes interact with dense canopies to significantly modify atmospheric flow. These interactions generate complex flow features, including recirculation zones, flow separation, and spatially variable turbulent transport, which challenge the interpretation of eddy-covariance measurements and the estimation of biosphere–atmosphere exchanges.
Using the PALM model system, simulations will be performed over both real and idealized topographies representative of the Amazon region. These simulations will be directly compared with high-resolution observations from the Amazon Tall Tower Observatory (ATTO), enabling the analysis of turbulent structures, fluxes, and boundary-layer processes across scales.
By linking observations and simulations, the project will clarify how topography and canopy structure jointly control atmospheric turbulence and scalar transport, and will contribute to improved parameterizations of forest–atmosphere exchanges in complex terrain.
This research lies at the intersection of atmospheric turbulence, biosphere–atmosphere exchanges, boundary-layer processes, and micrometeorology.
English, Italian, Portuguese
Candidates are expected to have a background in atmospheric science, environmental physics, fluid dynamics, or related disciplines.
Basic experience or strong interest in the following areas is desirable:
- Analysis of high-frequency atmospheric and scalar measurements (e.g., tower or micrometeorological data;
- Numerical modeling, particularly Large Eddy Simulations (LES) or similar approaches;
- Programming skills (e.g., R, Python, MATLAB, Fortran, or similar) and data analysis;
- Familiarity with boundary-layer meteorology, turbulence, and/or biosphere–atmosphere interactions.
Experience with high-performance computing (HPC) environments and handling large datasets is considered an asset. Motivation to work on interdisciplinary topics combining observations and modeling in complex terrain is essential.
Phd Sandwich: 2 -12
Post Doc: Min: 2 - 12