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countries. We also host a large data set of > 30,000 terrestrial insect species, based on DNA metabarcoding. Additionally, we have access to accompanying environmental data. These data sets provide a unique
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team and lead the development and application of machine learning methods to large-scale genomic data generated at IPK-Gatersleben, with a focus on the impact of genetic variation on gene regulation
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European sea basins over decadal timescales, due to coastal darkening (COD) and artificial light at night (ALAN), and will determine drivers, sources and impacts of these changes at both large and small
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using geographic information systems (GIS) and programming languages (e.g. Matlab, Python, R) and working with large data sets and data formats, such as netCDF, HDF, including analysis tools such as NCO
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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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. Central to its objectives is the development of methods for culturing tailor-made organoids, assembloids and co-organoids for inter-organ communication towards AI-supported large-scale / high-throughput
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. Central to its objectives is the development of methods for culturing tailor-made organoids, assembloids and co-organoids for inter-organ communication towards AI-supported large-scale / high-throughput
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models using experimental data for precise mapping of real processes Conducting detailed analyses of thermomechanical stresses in electrochemical converters using the finite element method (FEM
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. For more information, please contact: Dr. Ugaitz Elu (elu@fhi.mpg.de ) or Prof. Martin Wolf (wolf@fhi-berlin.mpg.de ) Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 14195 Berlin Germany Max
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important insights into clinically relevant aspects of cancer development and therapy Perform state-of-the-art in vivo experiments and large-scale drug and genetic screens Learn and apply a wide spectrum of