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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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-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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tools Supervising and guiding Master and PhD students Active participation in project meetings and events Presenting and publishing the research on an international stage Your Profile: As part of our
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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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conditions, to large field-scale experiments with wild and domestic grazers. These experiments will test hypotheses related to the effects on nutrient cycling of grazers with different body size and grazing
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to build a collaborative scientific carrier in computer science and medical data analysis at a German top-ranked university. Help to acquire, mentor and teach students (e.g., PhD, MSc, BSc, seminar series
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models and neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio
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to design and conduct experiments at various scales from laboratory manipulations of animal plant-soil systems including micro and mesocosms under varying environmental conditions, to large field-scale