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Max Planck Institute for Solar System Research, Göttingen | Gottingen, Niedersachsen | Germany | 9 days ago
techniques have enormous potential for the modeling, prediction, and control of nonlinear systems governed by partial differential equations. This project will focus on developing machine learning methods
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal
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gene-drug interactions. In order to decipher the genetic and gene-drug interactions, we use model systems like Drosophila, human cell cultures and patient-derived organoids in high-throughput screens
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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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of hydrological connectivity of soil moisture using gridded soil moisture data sets and data-driven approaches (e.g., complex network methods) Develop models to predict gatekeeper locations and their relationship
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gene-drug interactions. In order to decipher the genetic and gene-drug interactions, we use model systems like Drosophila, human cell cultures and patient-derived organoids in high-throughput screens