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Field
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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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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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semester Application deadline Will be announced on webpage: https://www.mgse.econ.uni-muenchen.de/application/index.html Tuition fees per semester in EUR None Combined Master's degree / PhD programme No
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Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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/ Qualification that is highly welcome in industry Further development of your personal strengths, e.g. via a comprehensive further training program; a structured program of continuing education and networking
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research community in an open, diverse and international work environment Scientific excellence and extensive professional networking opportunities A structured PhD program with a comprehensive range of
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international work environment Scientific excellence and extensive professional networking opportunities A structured PhD program with a comprehensive range of continuing education and networking opportunities