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) (Reference: 24-STB-PB4-PD) Understanding how different Earth system components affect the variation of life forms and how this diversity of life impacts tectonic, geomorphic, and climatic processes at various
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., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted in the Department of Microbiome Dynamics of Prof. Gianni Panagiotou of the Leibniz-HKI in
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information for ocean health, sustainable blue economy, and coastal climate risks, downstreaming the data flow from climate ensembles to coastal areas at different spatial resolutions and for selected areas, in
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 9 days ago
skills for data acquisition and analysis (e.g., Python, MATLAB, LabView) Excellent verbal and written communication skills in English Desirable skills Experience with experimental techniques, such as
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large multi-dimensional datasets using statistical tools such as positive matrix factorization (PMF) and cluster analysis Investigate the influence of different urban emission sectors on atmospheric
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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language processing (NLP), and fine-tuning techniques Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science
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resilience and its change over time in the past (based on Earth observation data), present and future (based on Earth system model simulations for different future scenarios, e.g. using the CMIP6 ensemble and
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resilience and its change over time in the past (based on Earth observation data), present and future (based on Earth system model simulations for different future scenarios, e.g. using the CMIP6 ensemble and