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Within the framework of the GENAI-X project, we are seeking a highly motivated PhD candidate (m/f/d) to contribute to the ecosystem dynamics subproject, which develops hybrid and data-driven approaches with a strong focus on improving the representation and generalization of ML models for the dynamics of Ecosystem-Atmosphere Carbon and Water Fluxes.
Job Responsibility:
Build hybrid models, process-based and deep learning models, to capture ecosystem flux dynamics across space and time
Develop generalizable models robust to climate variability, extremes, and concept shift
Explore equation discovery and dependency-testing ideas to obtain deterministic, interpretable representations of plant carbon allocation and plant water status
Integrate multiple Earth observation data, from eddy covariance, sap-flux, to high-resolution Earth observation satellite datasets for model training and evaluation
Publish and present results in an interdisciplinary research network
Requirements:
Master’s degree (or equivalent) in a quantitative discipline, Earth system/environmental sciences or engineering, physics, mathematics/statistics, computer/data science, geoinformatics, geoecology or related fields
Programming skills (Julia, Python, MATLAB) and ability to work with time series and/or geospatial data
Motivation to work at the interface of ML and environmental process understanding
curiosity for interpretable and robust modeling
ability to work both independently and in a team
Excellent oral and written communication skills in English
What we offer:
A scientifically stimulating PhD project, relevant for climate-change research, in a curious and diverse research team
Strong computational and scientific infrastructure, and collaboration across disciplines within the project, the department and extramural partners
A broad selection of learning opportunities Earth system sciences and soft skills