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hearing loss. However, current neural devices are large, complex, and invasive, and are therefore used by only a fraction of people who could benefit from them. The goal of NANeurO is to design new
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invasive sensing tools to monitor metabolites, oxygen, carbon dioxide, pH, and other parameters. Ideally, the methods can function in parallel and on a large scale. The research is vital to understand key
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sequencing, large-scale genomic, transcriptomic, proteomic, metabolomic, and phenotypic data) using cutting-edge technologies, such as machine learning You will perform transcriptomic and epigenetic analysis
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) is an advantage Experience in software tools such as Origin, Matlab, Python/Matplotlib or similar programs for data processing and evaluation. Knowledge of a relevant programming language complements
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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