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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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is led by Prof. Priyamvada Jadaun who is the Chair Professor of Electrical Engineering and Computer Sciences at TU Berlin. Prof. Jadaun also holds a Visiting Scholarship at University of California
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critical component analysis, and (iii) development of Automation of ML model and data selection. The applicants should have knowledge of machine learning and optical networks and willing to engage in testbed
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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models through specific activation functions. This project will be undertaken in collaboration with Dr Hemanth Saratchandran and Prof Simon Lucey of the Australian Institute for Machine Learning, and
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The Institute for Infection Prevention and Control (IPC, Head Prof. Dr. Philipp Henneke) is looking as soon as possible for a Bioinformatician (m/f/d, PhD) with focus on the analysis of large
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, Large Language Models, Human-Computer Interaction, Virtual reality. The selected candidate will work on the design and implementation of a human-computer interface to support education using an AI-based
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approach could resolve this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat
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to test data, which are therefore only useable within a narrow range of scenarios. These limitations result in the requirement of large number of high-cost experiments being conducted to populate the models