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Field
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, enabling energy-efficient, quiet, and long-duration monitoring of ecosystems. The research will integrate novel lightweight perception modalities for robust perching in the wild, agile control algorithms
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simulation of scenarios with different materials and geometries. - Support the development and implementation of signal and image processing algorithms, including fast inversion techniques, FFT, and nonlinear
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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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garden experiments to test how plant performance is determined by plant genotype, the environment (above- and belowground parasite and warming in different combinations and sequences), and their
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parasite and warming in different combinations and sequences), and their interaction. Plant performance will be assessed by measuring life history traits and fitness and using hyperspectral imaging of both
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. Design and implement multimodal unlearning techniques to address bias and privacy concerns. Evaluate the generalisability of multimodal learning across different socio-contexts. Validate the proposed
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
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to work independently as well as in teams Work in a structured way, set goals and make plans to achieve them, result-oriented Excellent analytical skills, analyze data, assess different perspectives and
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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skills and motivation to implement algorithms and test them in practice on large-scale problems. Programming Skills: You are proficient in at least one scientific programming language (such as Python