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) software for topology-informed biomedical image analysis and large foundation models. You will be responsible for Develop new machine learning algorithms for microscopy image analysis problems (2D/3D/4D/5D
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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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are developing algorithms and tools to address these bioimage analysis problems, which are all driven by real biomedical research. Depending on the background and interest, the student will have the opportunity
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-omics data sets generated with innovative high-throughput technologies used in Research Sections I and II (e.g. sensory, metabolome, proteome and transcriptome data) by using efficient algorithms and
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SEAGUARD This position is embedded in the SEAGUARD project (Seagrass Growth and Adaptation Using AI Research & Development), which focuses on assessing the CO₂ storage potential of seagrass meadows and
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for new quantum computing algorithms. It will rely on statistical structure learning represented by knowledge graphs and efficient low-rank tensor compressions. We are looking for: A completed scientific
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near-real-time forecast system for the Baltic Sea Generate high-resolution daily surface salinity maps for the Baltic Sea and validate them with available observational datasets Develop algorithms and
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group Computational Plant Biochemistry, which focuses on Computational Metabolomics and develops methods for processing and interpreting mass spectrometry data in the context of plant biochemistry