Markus Leippold

This postdoctoral position at ETH Zürich focuses on advancing the application of artificial intelligence (AI), large language models (LLMs), and machine learning to extract trustworthy climate information from large-scale geospatial and Earth system datasets. The research is embedded within the NCCR CLIM+ program, which aims to bridge climate science and AI by developing novel methods for climate data analysis, downscaling, and synthesis using foundation models such as Google Earth AI, AlphaEarth, and TerraTorch on a high-performance computing platform. The successful candidate will design, develop, and apply AI/ML methods—including LLMs and foundation models—to extract, synthesize, and reason about climate information from satellite, reanalysis, and climate model datasets. The role involves advanced physics-aware machine learning for kilometer-scale climate process representation, implementing generative AI models for high-resolution climate downscaling, data assimilation, emulation, and post-processing of weather-to-climate predictions. Methods for uncertainty quantification in projections of extreme weather and climate hazards will also be developed. The researcher will work with multimodal Earth system data, including satellite, airborne, in-situ, reanalysis, and high-resolution climate model outputs. Responsibilities include building robust, scalable software tools and data pipelines for processing large geospatial datasets, enabling CLIM+ project partners and the broader research community to leverage AI-driven climate information. Maintaining reproducible research workflows using modern software engineering practices (version control, testing, documentation, containerization) is expected. Original, publishable scientific research generating novel insights at the intersection of AI and climate science is a core component. The researcher will write and submit peer-reviewed journal papers, present at international conferences, and contribute to NCCR CLIM+ reporting and grant proposals. Collaboration with research groups at ETH Zürich (Prein, Knutti, Fischer) and the University of Zurich (Leippold, Vaghefi, Colesanti Senni) is integral to aligning AI development with climate science research questions. Participation in CLIM+ programme activities, workshops, and cross-project initiatives is encouraged, as is contributing to the supervision of students and interns. Applicants must hold a PhD in climate science, atmospheric science, computer science, data science, physics, applied mathematics, remote sensing, or a related field. A strong publication record in AI, machine learning, or Earth system data science is required. Experience with large geospatial datasets, remote sensing, reanalyses, or climate/weather model output is expected. Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX is necessary. Experience with HPC and GPU-accelerated computing, familiarity with foundation models and LLMs, and interest in reproducible workflows and scientific software development are preferred. Candidates should demonstrate an independent, proactive, and collaborative working style with strong communication skills. ETH Zürich offers an exciting interdisciplinary research environment at the forefront of climate science and AI, a collaborative and supportive team culture, strong support for career development, access to state-of-the-art computing infrastructure, and unique large-scale climate and geospatial datasets. Opportunities exist to develop independent research ideas and build a scientific profile. The institution values diversity, sustainability, and provides a modern, inclusive workplace with attractive employment conditions and flexible working arrangements. Applications must be submitted online via the ETH Zurich application portal. Required documents include a letter of motivation, CV with publication list, copies of relevant degree certificates, and contact details of two or three academic references. Applications sent by email or post will not be considered. All applications submitted before 11 May 2026 will be reviewed. For further information, contact Prof. Andreas Prein, Prof. Reto Knutti, or Prof. Markus Leippold (emails provided in the post).

This postdoctoral position at ETH Zürich focuses on advancing the application of artificial intelligence (AI), large language models (LLMs), and machine learning to extract trustworthy climate information from large-scale geospatial and Earth system datasets. The research is embedded within the NCCR CLIM+ program, which aims to bridge climate science and AI by developing novel methods for climate data analysis, downscaling, and synthesis using foundation models such as Google Earth AI, AlphaEarth, and TerraTorch on a high-performance computing platform. The successful candidate will design, develop, and apply AI/ML methods—including LLMs and foundation models—to extract, synthesize, and reason about climate information from satellite, reanalysis, and climate model datasets. The role involves advanced physics-aware machine learning for kilometer-scale climate process representation, implementing generative AI models for high-resolution climate downscaling, data assimilation, emulation, and post-processing of weather-to-climate predictions. Methods for uncertainty quantification in projections of extreme weather and climate hazards will be developed, and the candidate will work with multimodal Earth system data including satellite, airborne, in-situ, reanalysis, and high-resolution climate model outputs. The researcher will build robust, scalable software tools and data pipelines for processing large geospatial datasets, enabling CLIM+ project partners and the broader research community to leverage AI-driven climate information. Maintaining reproducible research workflows using modern software engineering practices (version control, testing, documentation, containerization) is expected. The candidate will conduct original, publishable scientific research generating novel insights at the intersection of AI and climate science, write and submit peer-reviewed journal papers, present at international conferences, and contribute to NCCR CLIM+ reporting and grant proposals. Collaboration is central to this position, with close interaction with research groups at ETH Zürich (Prof. Andreas Prein, Prof. Erich Fischer, Prof. Reto Knutti) and the University of Zurich (Prof. Markus Leippold). Participation in CLIM+ programme activities, workshops, and cross-project initiatives is encouraged, and the candidate may contribute to supervision of students and interns. Applicants must hold a PhD in climate science, atmospheric science, computer science, data science, physics, applied mathematics, remote sensing, or a related field. A strong publication record in AI, machine learning, or Earth system data science is required. Experience with large geospatial datasets, remote sensing, reanalyses, or climate/weather model output is expected. Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX is necessary. Experience with HPC and GPU-accelerated computing, familiarity with foundation models and LLMs, and interest in reproducible workflows and scientific software development are preferred. Candidates should demonstrate an independent, proactive, and collaborative working style with strong communication skills. ETH Zürich offers an exciting interdisciplinary research environment at the forefront of climate science and AI, a collaborative and supportive team culture, strong support for career development, access to state-of-the-art computing infrastructure, and opportunities to develop independent research ideas. The institution values diversity, sustainability, and provides attractive employment conditions and flexible working arrangements. Applications must be submitted online via the ETH Zurich application portal, including a letter of motivation, CV with publication list, copies of relevant degree certificates, and contact details of two or three academic references. Applications sent by email or post will not be considered. The deadline for applications is 11 May 2026. For further information, contact Prof. Andreas Prein ([email protected]), Prof. Reto Knutti ([email protected]), or Prof. Markus Leippold ([email protected]). Please note that questions regarding the position should be directed to these supervisors, but applications must be submitted through the online portal.