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Field
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-driven simulation with physics-inspired data and image analysis, often in close collaboration with experimental partners, to identify physical principles behind biological dynamics and self-organization
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key role in gene regulation. By employing omics-based approaches, metabolite tracing, proximity-labelling, and advanced imaging techniques, this project offers a unique opportunity to: Explore how
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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and spectroscopy Construction and optimization of single-molecule microscopy setups Development of image- and signal-processing software for single-molecule microscopy and spectroscopy data Analysis
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procedure Icon Applicants Applicants Icon Foundation Foundation Icon Notification Notification Nomination Examinationapprox. 1-2 months Review Processapprox. 3-4 months Conferral Nomination documents
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fully equipped department using high resolution live cell imaging techniques and image data analysis as well as atomic force microscopy combined with biochemical and molecular technologies. We foster
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architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
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an emphasis on in vivo assays, animal models and state-of-the-art imaging tools Soft skills seminars on communication, presentation and other topics equally relevant for achieving success in both a scientific
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Studying lipid alterations of tumorous material by mass spectrometry imaging Quantification of corresponding lipids using liquid chromatography mass spectrometry Data analysis of sequencing and microscopy
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Studying lipid alterations of tumorous material by mass spectrometry imaging Quantification of corresponding lipids using liquid chromatography mass spectrometry Data analysis of sequencing and microscopy