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skills and curiosity about complex systems. Position Overview You will design and implement new computational and statistical models to reverse-engineer causal networks from noisy, high-dimensional, multi
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controlling cancer cell biology. Your research focus will be on (1) the molecular mechanisms underlying the action of the oncoprotein MYC; or 2) the role of chromatin remodeling complexes in regulating access
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of the Microverse” (https://www.microverse-cluster.de/en/# ), the CRC/Transregio 124 “Pathogenic Fungi and Their Human Host: Networks of Interaction” (https://www.funginet.de/willkommen.html# ) funded by the Deutsche
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of modeling higher-order relational structures such as hypergraphs and simplicial complexes, which are prevalent in complex biological systems like protein assemblies, signaling pathways, and metabolic networks
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few kilometers using new computer science methods, particularly machine learning. This involves the analysis of very complex spatiotemporal phenomena, especially so-called submesoscale processes
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, and report relevant data. Direct data collection. Present research to colleagues within and outside the institution; draft a complex manuscript with minimal supervision, as required. Perform other
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(NGS) technologies, including single-cell sequencing, to interrogate transcriptional networks. A central focus of your work will be the systematic analysis and interpretation of multi-omic datasets
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analytical sciences. We are looking for talented people to join us. Your responsibilities include: Interdisciplinary research within the project "Complex and Competing Phenomena in Recycled Flame-retardant
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part of the MARDATA doctoral network, the project “AI-derived thermodynamic parameters for aqueous modelling (AI-queous)” invites applications for a PhD position at the intersection of Computer Science
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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