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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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(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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will have the opportunity to: Gain important insights into clinically relevant aspects of cancer development and therapy Perform state-of-the-art in vivo experiments and large-scale drug and genetic
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analyzing data from large cohorts, ideally within the NAKO German National Cohort Experience in teaching epidemiology to students Desirable qualifications Experience in the field of metabolic diseases
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data mining. The group provides a strong network to local AI expertise (e. g. Hessian.AI, TU Darmstadt), large scale compute infrastructure, as well as a broad international network (Stanford, UC San
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the integration of large-scale biological datasets derived from both the host and the microbiome, employing advanced statistical methods and cutting-edge artificial intelligence techniques to uncover novel insights
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Large-scale and in-depth characterization of optimized Channelrhodopsin variants for basic research in neuroscience and future optogenetic therapies Automated (Syncropatch384, Nanion) and manual patch
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tasks You will carry out research tasks in Prof. Wende's working group, particularly in the field of element-specific investigation of functional magnetic materials at large research facilities. In
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of Freiburg, founded in 1457, belongs to the best universities of Germany. Freiburg has a large international studen body and is located in the Black Forrest with a 30-minute drive to both Switzerland and
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, including but not limited to cancer, metabolic disorders, and infections. The research design involves the integration of large-scale biological datasets derived from both the host and the microbiome