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Field
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herbivores space use behavior in relation to snow conditions, data is required at cm to m resolution. This PhD project will develop and apply remote sensing methods to advance terrestrial snow monitoring
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in refining hydrocarbons. Emerging optical sensing technologies, including quantum gas LiDAR, offer the ability to detect methane concentrations remotely with unprecedented sensitivity and spatial
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into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic
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quantify these changes, identify their causes and describe their impacts on biodiversity and ecosystem services. To do this we use a combination of diverse methods, from empirical research to remote sensing
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programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication skills, and excellent English. Relevant
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-term contract is based on § 2 WissZeitVG. Your Tasks: You will conduct research in the field of experimental atmospheric physics, with a focus on optical remote sensing of the atmosphere up to the edge
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cutting-edge methods, models and technologies in environmental science, quaternary sciences, bedrock geology, paleontology, physical geography, biodiversity and ecosystem science, remote sensing, Geographic
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and describe their impacts on biodiversity and ecosystem services. To do this we use a combination of diverse methods, from empirical research to remote sensing and simulation modeling. A quantitative
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with data analysis/modelling and programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication
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PhD Positions Application Deadline 1 May 2026 - 23:59 (Europe/Brussels) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2026 Is the job funded