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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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(Shannon entropy, compressibility, effective complexity and logical depth). The data basis for the analyses are two-fold: first, recent connectome data, i. e. large datasets of all synaptic connections in
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-specialist users. This will require extensive scientific data analyses with regard to socially relevant issues. Furthermore, large amounts of data from different disciplinary contexts must be compiled and
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or PhD) are desirable. Proven experience in managing clinical or epidemiological study data, preferably including large cohort studies with omics data. Familiarity with electronic data capture systems
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of Crop Species”. Close collaboration with the IPK genebank is expressly desired. In particular, large sequence data sets from gene bank accessions shall be systematically used for research and further
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Health, Chemical Sciences and Sustainability, Trustworthy Data Science and Security, and Future Energy Materials and Systems. In addition, the Research Alliance has established the College for Social
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University professorship (m/f/d) in 'AI in Occupational, Social and Preventive Medicine' (salary gra
. Evaluation of large datasets (big data), for example from health insurance companies or large corporations, in the context of health and prevention. Requirement profile: A relevant academic degree (e.g. in
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(EKFZ: https://ekfz.uni-goettingen.de) Your tasks Large-scale and in-depth characterization of optimized Channelrhodopsin variants for basic research in neuroscience and future optogenetic therapies
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, image analysis, and/or biophysical measurement techniques experience in team leadership or research infrastructure management is a big plus excellent communication and team coordination skills service
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expertise from computer science and mathematics into the field of ocean sciences. The school’s interdisciplinary focus spans supercomputing, modeling, (bio)informatics, robotics, statistics, and big data