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algorithms to compute similarity between interaction interfaces across millions of comparisons. This hinders identification of novel modes of protein binding, i.e. those predicted by AlphaFold, and it hinders
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
self-assemble from a number of interacting single-stranded DNA molecules. An accurate prediction of DNA structures still remains difficult, which significantly slows down the development of new desirable
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interaction interfaces across millions of comparisons. This hinders identification of novel modes of protein binding, i.e. those predicted by AlphaFold, and it hinders design of more balanced datasets of
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Multidimensional Omics Data Analysis You will be responsible for Prediction of metabolic activity in complex microbial communities, leveraging
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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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-driven simulations, optical remote sensing and biogeochemical modeling to predict seagrass distribution under various climate and nutrient scenarios. SEAGUARD aims to provide science-based recommendations
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developmental, stem cell and cell biology systems) to predict, observe, and manipulate epigenetic processes. https://www.helmholtz-muenchen.de/ife The Schneider group is recruiting a PhD student to work on the
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conceptual framework for predicting the role of microplastic as a catalyst or an antagonist of drought. The candidate will be part of the international and interdisciplinary Plant Ecology group
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approaches in the powerful model systems zebrafish and fruit fly, and structural biology (including AlphaFold predictions and cryo-EM), we will dissect the roles of these novel mRNA export regulators and
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 24 days ago
models as predictive tools to address questions regarding the response of deep-sea ecosystems to various pressures. A key question addresses the best combination of ML and network analysis to maximize