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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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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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. 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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thesis in Artificial Intelligence and Cyber-Physical Systems, with a strong emphasis on Explainable AI. This PhD will investigate how CPS can clearly explain their proactivity learning, the reasoning
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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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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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for hydroclimate extremes. Position You will work actively on the preparation and defence of a PhD thesis focusing on the analysis of extreme events using deep learning methods. Your responsibilities will include
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the preparation and defence of a PhD thesis focusing on the analysis of extreme events using deep learning methods. Your responsibilities will include developing deep learning-based methods for modeling extreme
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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