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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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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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robotic systems and AI models. You will learn how to programme advanced robotic systems and how to implement aspects of deep learning and neural networks for chemical property prediction. You will be part
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the context of stress and depression? Are you intrigued to learn more about early-life stress as risk factor for depression and the role of the endocannabinoid system? Do you enjoy working in a lab with
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underlying health and healthcare. They collaborate with partners from all over the world to improve health outcomes for all. Radboudumc unites patient care, research, education, and corporate learning, which
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Are you fascinated by understanding fundamental neurobiological processes in the context of stress and depression? Are you intrigued to learn more about early-life stress as risk factor for
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provide to infrastructure managers You will be part of a multidisciplinary project with leaders in the domain of infrastructure governance and modelling. You will learn to conduct research independently and
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an advantage. Knowledge on programming skills (Matlab, C) are considered an advantage. You are willing to learn MRI and X-ray CT imaging methods. Do you like to collaborate with industrial partners? Are you
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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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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning