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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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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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(ECLECTX team). This person occupying this position is planned to work on modeling computing elements, established and emerging, at different levels of abstraction, design and development simulation tools
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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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technology. ▪ Close connection to the activities of the Munich Quantum Valley with its main goal to build a quantum computer based on different platforms, to develop suitable algorithms and applications, and
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning
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03.06.2021, Academic staff The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved