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Field
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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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Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C++); Proven interest in
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geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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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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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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artificial intelligence, computational cognitive science, human-computer interaction, computer science, information systems, or another relevant field. - A keen interest in pursuing interdisciplinary research
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hold an MSc degree in environmental science or ecology, with a proven expertise in data analysis, organizing and handling. Expertise in machine learning is a plus. A sound command of the English language
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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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