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Isaac Sim, ensuring realistic physics for hybrid locomotion. You will develop and train RL algorithms for hybrid locomotion tasks, including transitioning between locomotion modes and balancing on uneven
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guidelines for internship remuneration. The duration of the thesis depends on the requirements of your university. The Fraunhofer-Gesellschaft attaches great importance to the professional equality of women
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classification algorithms need to be extended with the capability to detect out-of-distribution environments and to autonomously infer their traversability. In this thesis, you will design a semantic
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for the employment of auxiliary staff. The position is initially limited to one year. The monthly working hours are 40 hours. The Fraunhofer-Gesellschaft attaches great importance to the professional equality of women
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, the objective of this thesis is to develop and test a real-time, tightly-coupled traversability algorithm that fuses information from both sensor modalities, therefore providing a more complete understanding of
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The Fraunhofer Institute for Algorithms and Scientific Computing SCAI in Sankt Augustin, near Bonn, has around 180 employees who research and develop innovative methods in the field of computational
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effectively over extended distances. For this purpose, it is essential to develop a robust mapping algorithm, that allows for continuous, online map growth during exploration and mapping operations. The map
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that can be used to compare the arrangements • You select suitable algorithms to evaluate the quality of the PPG signals (machine learning, Python) What we offer We offer you a challenging and varied
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of change Engage in the research, and test the latest machine learning algorithms Analysing, preprocessing, and visualizing different data types (texts, images,etc.) Review latest literature and data
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, computer vision, pointcloud-processing or machine learning is a plus Analytical mindset and experience in algorithm development Enthusiasm for mobile robotics Fluent in English or German What you can expect