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industrial Ph.D. position focused on developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies (ONT) long-read sequencing data
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work actively on the preparation and defence of a PhD thesis in the crossroads between the fields of robotics, signal processing and machine learning The candidate will explore how graph-based
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of Applied Engineering is looking for a full-time (100%) doctoral scholarship holder in the field of in-air acoustic sensing and applied machine learning for building the next-generation of intelligent robotic
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), user interface design, or data visualization techniques. Familiarity with frameworks for explainable machine learning (e.g., SHAP, LIME, Captum, Alibi). Experience in designing context-aware, adaptive
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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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. You are fluent in Python, machine learning, and deep-learning tools (e.g., TensorFlow, PyTorch). You can speak and write fluently in English. A background in hydroclimate extreme event analysis is
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. You can work in a group as well as on your own initiative. You have knowledge in machine learning for vision. Hands-on experience with image acquisitions and different types of cameras (visible
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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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machine learning for vision. Hands-on experience with image acquisitions and different types of cameras (visible, infrared, RGB-D, etc.) is highly valued. You can demonstrate excellent study results. Your
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, trustworthy, and fully explainable. The project introduces Generative Learning Cognitive Services (GLCS), intelligent, modular CPS components combining generative eco-cognition, cognition-oriented proactivity