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Field
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-Disciplinary Collaboration: Immerse yourself in a highly collaborative and interdisciplinary research environment, where you'll work alongside experts from fields such as engineering, data science, urban studies
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analytical chemistry, mass spectrometry, or trace gas measurements is an asset Enthusiasm for laboratory and field work Willingness to engage in programming and data analysis (preferably Python) Excellent
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cell sequencing datasets and to combine these data with AI analyses from bone marrow smears. We aim to analyze mechanisms and also to develop new biomarkers for therapy response and resistance. Job-ID
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being carried out in close collaboration with Dr. Alexei Kiselev and his research team at the Karlsruhe Institute of Technology. The Department of Atmospheric Microphysics is looking for a committed
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 2 months ago
Physics invites applications from talented, ambitious graduating MSc students for a PhD position on Exploring Supernova Remnants in eROSITA and Multiwavelength Data (m/f/d) Job Code: 16/2025 The PhD project
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around 30,000 people in nearly 15,000 households. SOEP aims to capture social change and thus handles a constant stream of new and diverse topics and tasks. Its data collection and generation adhere
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@mpinat.mpg.de Apply now Max Planck Institute for Multidisciplinary Sciences Research Group for Sustainable Biocatalysis Dr. Andreas M. Küffner Am Faßberg 11 37077 Göttingen Germany www.mpinat.mpg.de/ Information
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Your Job: The work is part of the joint project “Towards sustainable production by regenerative agriculture and agroecology in a changing climate” (ReGenFarm). Together with Bayer Crop Science, IBG
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, Data Science, or a closely related discipline Strong interest in interdisciplinary research at the interface of Artificial Intelligence, Data Science, and Life Sciences Excellent programming skills
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available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling