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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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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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-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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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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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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focus on neutron spectroscopy as main analysis technique, supported by complementary experimental techniques or theoretical simulations Hands-on participation in experiments at large scale facilities as
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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