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PhD Position - Data-Driven Modeling of Watershed Dynamics for Resilient Water Management Systems

Overview
As Global Warming intensifies, droughts are projected to increase in frequency, duration, and severity. In this context, effective water management will be crucial to ensuring a reliable supply of this vital resource. To comprehend the journey of water from rainfall to various supply and distribution systems, it is essential to elucidate the relationship between precipitation and the factors influencing watersheds' capacity to capture water, such as terrain characteristics, land use, urbanization, forest density, and meteorological conditions to mention a few.
This proposal aims to leverage satellite data on cumulative daily rainfall, alongside watershed and meteorological characteristics and river flow measurements, to understand the role of each feature on the basin’s ability to catch rainfall. The resulting models will guide the development of strategies for building more resilient water distribution systems, ensuring an adequate supply of this increasingly scarce resource.

Candidate 
The ideal candidate for this PhD position should have a solid understanding of data science and a keen interest in environmental applications. Basic knowledge of statistical analysis and some experience with machine learning and data-driven methodologies is desirable. Familiarity with programming languages such as Python or R, and an interest in geospatial analysis tools and remote sensing data, will be advantageous. While understanding of hydrology and climate science is not required, a willingness to learn and engage with these topics is advantageous. The candidate should possess good analytical skills, attention to detail, and the ability to work both independently and as part of a multidisciplinary team. Strong communication skills, both written and oral, are important to effectively share research findings. A bachelor's and/or master’s degree in a related field, such as environmental science, geophysics, data science or a similar discipline is required.

Call of the public competition for the contracting of Trainee Predoctoral Research Staff 2024 (PIPF): https://www.urv.cat/en/research/support/programmes/urv/programa-marti-franques/pipf/call-of-the-public-competition-for-the-contracting-of-trainee-predoctoral-research-staff-2024-pipf/

Researchers willing to apply should check that they fulfill the eligibility criteria.

The candidate should apply for the fellowship with code 2024PMF-PIPF-22 in the link: https://appsrecerca.urv.cat/cgi-bin/programes/application/inici.cgi?conv=2024PMF-PIPF-&fase=1&idioma=ENG

CONTACT
Alexandre Fabregat — [log in para visualizar]
Anton Vernet — [log in para visualizar]
Lluís Vázquez — [log in para visualizar] 

University Rovira i Virgili - Tarragona, Spain