68 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Technical University of Denmark in Denmark
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Job Description We are seeking a highly motivated and talented Postdoctoral Researcher to join our team and contribute to our cutting-edge quantum computing project. This ambitious project focuses
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Job Description If you have a keen interest in computer vision, machine learning, and deep learning, and their application in species identification and automated catch registration
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qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning/deep
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equivalent). A successful candidate will have a solid background in at least two of the following areas: Computer vision for object detection and/or semantic segmentation Anomaly Detection Efficient multi
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equivalent). A successful candidate will have a solid background in at least two of the following areas: Computer vision for object detection and/or semantic segmentation Anomaly Detection Efficient multi
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Job Description If you have a keen interest in computer vision, machine learning, and deep learning, and their application in species identification and tracking to study animal responses to fishing
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. Co-supervising PhD & MSc students, particularly those working in electrophysiology and computational modelling. Developing an online tinnitus test battery and database Stratify tinnitus phenotypes
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Job Description If you have a keen interest in computer vision, machine learning, and deep learning, and their application in species identification and tracking to study animal responses to fishing
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, retrapping, tunnelling, and recombination, based on their respective transition probabilities. Use machine learning approaches to optimize model performance and run simulations over multiple time scales
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electronics, fluidics system, and data collection hardware (based osingle-board computer systems, e.g., ESP32 or similar). Demonstrate prototype applicability under simulated lab-based and real-life conditions