579 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions in Netherlands
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concepts into measurable, reusable methods. Context: You will contribute to the ERC Starting Grant GAMECHAR (Scalable AI-Driven Framework for Gameplay Characterization; https://cordis.europa.eu/project/id
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differences in learning, memory, and processing between these systems. This project develops the necessary methods to study how smart AI-models are compared to people, now and in the future, and sheds light on
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on the individual DC projects can be found on the consortium website: www.integrate-dn.eu . Where to apply Website https://www.academictransfer.com/en/jobs/358012/16-phd-positions-to-advance-psy… Requirements
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are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Artificial Intelligence / Machine Learning for ECSS space standards requirements
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Breukelen, Dr. Yvette de Haas, Prof. Dr. Roel Veerkamp (WUR-ABG). See the link to site Chairgroup Animal Breeding and Genomics Group | WUR . Where to apply Website https://www.academictransfer.com/en/jobs
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at scientific conferences and by writing scientific articles and a PhD thesis; attending PhD courses as part of the graduate education program. Where to apply Website https://www.academictransfer.com/en/jobs
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group? Do you enjoy creating complex machines that have never existed before? Do you want to explore physics that nobody else has seen? If yes, then you might want to join the Ultracold Strontium Group
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to develop synthetic cells that mimic tubular function by integrating transport proteins in lipid membranes. Furthermore, the ultimate long-term goal is to integrate these cells in a dialysis machines. The
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning
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, solvent-based recycling process for complex plastic waste streams such as multilayer packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process