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Field
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Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on
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theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading
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with exceptional precision and in unexplored energy regimes. We use a crossed molecular beam machine with a Zeeman decelerator, which enables precise control over the velocities and quantum states of
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interest in neuro-behavioral sciences and a passion for behavioral signals. Demonstrable experience in advanced data analysis and data collection. Familiarity with machine learning and proficiency in Python
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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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(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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researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be
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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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-selected atoms and molecules with exceptional precision and in unexplored energy regimes. We use a crossed molecular beam machine with a Zeeman decelerator, which enables precise control over the velocities
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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