218 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" PhD scholarships in Netherlands
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and peers across Europe, gain exposure to diverse academic and professional environments, and engage actively in dissemination, workshops, and scientific communication. Where to apply Website https
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computer models as well as being in the lab. You have affinity with analytical determination of pharmaceuticals in difficult matrices. You are able to dive into topics outside your current knowledge base
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you to apply. Your application will receive fair consideration. Challenge. Change. Impact! Faculty Mechanical Engineering From chip to ship. From machine to human being. From idea to solution. Driven by
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of theoretical computer science researchers in Amsterdam (https://theory.amsterdam ). What are you going to do? You are expected to: carry out original research in the field of theoretical computer science; have
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on the interface of topology and higher algebra (https://www.math.ru.nl/symmetry/ ), based at Radboud University Nijmegen, Utrecht University, and Vrije Universiteit Amsterdam. This community meets frequently
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for applicants: https://phdpositionswetsus.eu/guide-for-applicants/ Where to apply Website https://phdpositionswetsus.eu/ Requirements Research FieldAgricultural sciences » Soil scienceEducation LevelMaster
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degree in Biomedical Sciences, Molecular Biology, Physiology, or a related field. Affinity with stem cell and cardiac biology – You have experience with, or a strong desire to learn, hiPSC culture
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via the application webpage before the deadline will be considered eligible. Guidelines for applicants: https://phdpositionswetsus.eu/guide-for-applicants/ Where to apply Website https
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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint