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systems, mainly plants. The BM^2 Lab is mainly computational and uses ad-hoc developed modeling tools such as MorphoMechanX to provide explanatory and predictive scenarios for developmental problems. We
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regulation, RNA biology, protein biochemistry, stem cells, bioinformatics, sequence analysis, mathematics, statistics, molecular evolution, or biophysics, and wish to work at the Max Planck Institute
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Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
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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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groups are strongly encouraged to apply. Your Tasks: Development and application of algorithms for modelling, evaluation and visualization of ultrafast processes Investigation of ultrafast dynamics in
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure, improved mechanical and corrosion properties. Research stays are planned
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: Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when
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and in-house developed software to predict structures of interacting proteins and in collaboration with the Steinegger lab, developed highly efficient AI-based algorithms to compute similarity between
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sensitivity analysis, impact of the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy
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potential projects: Development of modern auto-differentiation (JAX-based) physics simulators for the discovery of new physics experiments) Developing, benchmarking and advancing state-of-the-art AI-driven