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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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work hands-on with clinical data and build robust deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image
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In this role, you will help develop and implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical
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. Your work may involve formulating new models, analysing structural properties, and developing innovative algorithms with both theoretical rigor and practical relevance.
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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
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-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
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, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor segmentation, enabling
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deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement
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within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
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interpretation is subjective, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor