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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 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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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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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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biochemists developing the labeling agents, data analysts developing analysis algorithms and physicists developing hardware. The candidate The candidate should have a firm base in in vivo imaging and cell
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“Stability and Solvability in Deep Learning”. This project focuses on mathematically analyzing machine learning algorithms with a particular focus on questions of stability, computability, and robustness
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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Planning of and participation in (RMT) field experiments in Germany, Europe and worldwide Further development of the processing algorithm for RMT data and integration into the analysis software available
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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the imaging system for generating image datasets from agricultural machines Develop and implement computer vision algorithms for plant disease detection and health estimation in soybeans Design and