569 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" positions in Netherlands
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their learning and develop reading comprehension skills? Are you eager to combine learning sciences research with classroom innovation? Then this PhD position may be of interest to you. Job description The Erasmus
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transport proteins in lipid membranes. Furthermore, the ultimate long-term goal is to integrate these cells in a dialysis machines. The objectives for this position are to: Formulate synthetic cells with
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; interfacing with the scientific community for mission exploitation; collection and management of the operational and exploitation data; assessment of the mission objectives, lessons learned and recommendations
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, recommendations and lessons learned for future phases of Celeste and LEO-PNT in general. You will also be responsible for identifying, assessing, managing and reporting the health and safety risks in your area of
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performance, developing your people by encouraging learning, delegating responsibility and giving regular and constructive feedback; Strong problem-solving skills to deal with day-to-day operational challenges
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layers across heterogeneous platforms. Ideally you would have familiarity with deploying or supporting AI and machine-learning workflows, including large models, large network analytics, or multimodal data
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Liefvoort. For questions about this particular research project, please contact Cécile de Morrée. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about
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now Do you want to shrink the gap between artificial and biological intelligence? In this research assistantship you will work on the development of state-of-the-art machine learning approaches
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modern AI techniques and methods or desire to work on Applied Machine Learning Problems > Constantly questions finance/trading data and stays motivated to seek answers despite most often proving
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, including abstract geospatial workflows; design AI- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute