70 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" PhD scholarships in Netherlands
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high-impact journals and present at international conferences. Where to apply Website https://www.academictransfer.com/en/jobs/357571/phd-on-mixed-signal-circuit-des… Requirements Specific Requirements A
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16 Jan 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer architecture Computer science » Computer hardware Researcher
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disconnectivity in brain networks relates to symptom networks and recovery trajectories in psychiatric patients. Apply and further develop methods from network science, machine learning, and computational
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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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? Maybe you want to join our team as a PhD on our journey
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quantitative methodological skills in handling detailed spatial data, including various econometric techniques and machine learning approaches; a thorough understanding of empirical, explanatory research; a
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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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preferably with data analysis and machine learning (e.g., Python, AI frameworks). You have strong analytical and problem-solving skills, with the ability to translate complex clinical processes into structured
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artificial intelligence, computer science, engineering, mathematics, physics, or a related discipline Demonstratable background in machine learning, information retrieval or natural language processing
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will