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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
Area of research: Laborkräfte Job description: Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic nodule fields (m/f/d) Background While some companies
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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supported by an external team of deep-learning experts. You will also become an integral part of the Multiscale Cloud Physics Group currently being established by Dr Franziska Glassmeier at the Max Planck
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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science, and applied plant research Example reading: Peleke, F. F., Zumkeller, S. M., Gültas, M., Schmitt, A., & Szymański, J. (2024). Deep learning the cis-regulatory code for gene expression in selected
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both. Ideally, you bring strong technical expertise and the curiosity to work across modalities and domains. Your responsibilities Design and implement machine learning and deep learning pipelines
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Methodological competence: Strong programming skills, ideally including experience in deep learning and/or high-performance computing Passion for method development Very good English communication skills – both
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., deep learning and statistical modeling). You have knowledge of molecular genetics and genomics. You have a very good command of English (both spoken and written). You have the proven ability to conduct
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weekly working time of 40 hours per week. The position can be filled on a part-time basis. Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use