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to independently develop ideas for new research activities The following additional knowledge would be positive: Good knowledge of DSP algorithms for digital coherent transmission Experience with VPIphotonics
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Engineering, or similar. In the research group “Navigation of Mobile Robots” we develop autonomous, mobile robots for a variety of outdoor applications, such as agriculture, forestry and logistics. The focus is
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University of Darmstadt seeks candidates at the earliest possible date for two Professorships for Algorithmic Graph Theory and Logic. The positions are limited to a period of six years and come without
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obstacles blocking the view. You will develop a global pointcloud mapping pipeline consisting of a pointcloud filtering approach and a pointcloud matching algorithm. The filtering approach should utilise
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into the setting of metabolic flux inference and, with inspiration from existing algorithms, develop tailored MCMC algorithms. You will implement the ensuing algorithms in an existing C++ framework, validate and
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-inspired, method-driven theory to application-driven research. Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms
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, or machine learning is an advantage Analytical mindset and experience in algorithm development Enthusiasm for mobile robotics Excellent German or English language skills What we offer Cutting-edge technology
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the necessary optics. Develop real-time MIMO DSP algorithms (implementable on FPGA) for crosstalk mitigation in the multicore fiber. What you contribute Outstanding scientific Master's degree in electrical
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and make energy use more efficient. We develop novel optimization methods, Machine Learning algorithms, and prototypical Energy Management systems (EMS) controlling complex energy systems like buildings
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. Your task in detail: Design a data pipeline to extract and preprocess activity-relevant features from monitored building power consumption data Develop a classification algorithm (e.g., based on Gaussian