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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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maintaining human oversight for quality and accuracy. This real-world case provides a unique opportunity to study how trust in AI systems develops, whether specialists seek to preserve authority in decision
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learning, with a particular focus on differential equation-driven frameworks. The research will be fundamentally oriented, and the overall mission is to develop computationally efficient and statistically
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predict food-effector systems. Key Responsibilities • Develop graph-based (multi-)omics analysis algorithms • Benchmark graph-theoretic against graph-ML approaches • Analysis of food-related (multi-)omics
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and IT, to develop techniques for planning the work of Agri robots, scheduling their tasks, deploying IoT devices in fields as well as for collecting, integrating, storing, analysing, building
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project aims to develop advanced control and planning algorithms that enhance robustness and safety, ensuring reliable performance even in the presence of magnetic fields and other uncertain conditions. The
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uncertainties, with a strong focus on safety-critical bilateral teleoperation (e.g., tele-surgery). The doctoral research will focus on the development of fault-tolerant and adaptive control architectures
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” because early abnormalities are missed by current diagnostic methods. This project sits at the cutting edge of biomedical engineering, AI, and respiratory medicine, developing a non-invasive, low-cost
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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UiA-CERN PhD Position in Multi-robot Mapping and Environmental Data Sharing - Uncertain Environments
representation, efficient transmission strategies tailored to mission requirements, and algorithms for combining data from multiple sources to improve accuracy and visibility. The project will also explore