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Field
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Foundation Models to perform traversability inference on incoming camera images. However, a major challenge lies in the lack of extensive datasets for the different field environments and the high effort in
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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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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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of different faiths and beliefs. Grounded in the Christian view of human life, the KU aims to create an academic and educational culture of responsibility. The research group Reliable Machine Learning at the KU
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: Implement and setup an algorithm to specify grasp poses for different parts with a specific robotic hand (position, orientation, approach vector, pre-grasp offset), contact constraints, force/torque limits
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sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR as well as small-scale EMI measurements with root and shoot observations in controlled experiments
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the next era of the quantum revolution. Together, we research, develop, and design innovative quantum algorithms that push the boundaries of what is possible- especially in the natural sciences such as
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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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using electromagnetic induction (EMI), and ground penetrating radar (GPR) will be combined with soil sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR
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that process planning has a high potential for automated optimization. Building upon this, you will advance our optimization pipeline and evaluate different optimization algorithms/strategies. What you will do