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pursuing a PhD on the intersection of Bayesian Machine Learning and Wireless Communications. The successful candidate will work towards developing and analyzing intelligent autonomous systems (agents
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Department(s) Electrical Engineering Reference number V36.5600 Job description This research program is aimed at developing modern machine learning methods that lead to improved performance of audio
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-to-end network slicing requirements considering both the core networking and wireless access. In particular, the application of novel machine learning techniques such as Deep reinforcement learning (DRL
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available design space by inverting structure-property maps. Topology optimization as well as machine learning techniques will be considered. (iii) Applications including shape morphing and self-folding
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the development of new optimization methods (using machine learning, operations research, AI, deep reinforcement learning) needed for solving challenging operations management problems in, for example
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, Computer Science, or a related field. Knowledge and experience with simulation, digital twin and machine learning techniques are highly appreciated. Strong analytical and mathematical skills and demonstrated
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requirements: A PhD degree (or equivalent) in Electrical Engineering, Computer Science, Robotics, or related disciplines. Solid background on computer vision and machine learning for visual object recognition
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offered by data-driven machine learning approaches. Both strategies have classically been considered separately, despite that they often provide complementary descrptions of the same reality. The group will
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artifacts in image processing output in order to further improve the quality of our deliveries. One of the planned improvement steps is the integration of machine learning algorithms during regression testing
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devices, and electronic circuits Solid theoretical and applied knowledge of machine learning Solid skills in one or two programming languages e.g. OMNET, Python, and VHDL, Verilog HDL FPGA programming