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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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Germany Application Deadline 21 Sep 2025 - 21:59 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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to consider the Ericksen—Leslie model with stochastic fluctuations and investigate it in terms of existence of solutions and their approximation, the associated large deviation principle and the simulation
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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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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experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
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multidisciplinary team from several project partners (incl. IPK Gatersleben and Justus-Liebig-Universität Gießen) to analyze how controlled abiotic stress regimes (drought and flooding under different seasonal
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– annual special payment – collectively agreed vacation entitlement – company pension plan Senckenberg is committed to diversity. We benefit from the different expertise, perspectives and personalities
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 12 days ago
A doctoral degree in neuroscience or a related STEM field Prior experience in systems neuroscience using animal models A solid foundation in quantitative data analysis and statistics Programming
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and