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Field
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integrate large-scale sequence and RNA-seq data from internal and public resources. You build a reference library of predictive regulatory motifs. You use network analysis and random-forest approaches
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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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engineering methods (e.g., via CRISPR). Plan and execute experiments to probe robustness of tissue morphogenesis, particularly through quantitative imaging and large-scale molecular profiling (e.g., via scRNA
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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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is expected, because large software toolboxes are used and further developed. The working language at the institute is English. Experience with targeted and optimized brain stimulation and with
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algorithms in extremely complex and enormously large spaces motivated by physics and chemistry Developing interpretable AI for scientific discovery in physics (example here ) Formal mathematics (using Lean’s
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-reconstructions and observations, low-order data assimilation, or deep neural networks. A quantification of the impact of mesoscale and submesocale features is also expected. At a later stage, the successful
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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Heidelberg University and Stanford University, including population health researchers, clinicians, and methodologists. The researcher will lead analyses in large-scale electronic health record data
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data. Please refer to our data protection information in accordance with Art. 13 of the General Data Protection Regulation (DSGVO) https://portal.mytum.de/kompass/datenschutz/Bewerbung/ regarding