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two distinct images from a single PET acquisition. Within this project, we will jointly develop, adapt and implement advanced image reconstruction algorithms in our in-house reconstruction software
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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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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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science, physics, or related fields Coursework in algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong
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Perform numerical modeling and validation of brain-inspired and neuromorphic algorithms Design, set up, and operate experimental systems for circuit-level measurements and data analysis Your Profile
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their thesis work in the field of robotics; Strong programming skills in C++ and/or Python, as well as experience in implementing robot learning algorithms; A strong background in control theory, machine
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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
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++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
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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 with defined microstructure
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning