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(PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings. Recent advances in machine learning for audio have focused on learning
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bipedal robot that will learn to walk on soft and natural ground, such as sand and gravel. The controller design will include knowledge of the type of ground the robot walks over, and how the substrate
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with attention to quality, integrity, creativity, and cooperation. You are fluent in Python, machine learning, and deep-learning tools (e.g., TensorFlow, PyTorch). You can speak and write fluently in
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with large-scale data analysis, such as genomics or transcriptomics data Experience with a workflow management system such as Snakemake or Nextflow A willingness to learn and apply machine learning
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breakdown spectroscopy (LIBS) and Raman spectroscopy) on metals and impurities • Development of a miniaturized laboratory setup for combined LIBS and Raman spectroscopy • Advanced machine learning
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or Nextflow A willingness to learn and apply machine learning approaches Offer A doctoral scholarship for a period of 1 year to start, with the possibility of renewal for a further three-year period after
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