73 machine-learning-"https:" "https:" "https:" "https:" PhD scholarships in Netherlands
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- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
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- University of Amsterdam (UvA)
- Vrije Universiteit Amsterdam (VU)
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- DIFFER
- DIFFER; Published yesterday
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); today published
- Eindhoven University of Technology
- Erasmus University Rotterdam (EUR)
- Leiden University; yesterday published
- Princess Máxima Center for Pediatric Oncology
- Radboud University Medical Center (Radboudumc)
- University Medical Center Groningen
- University of Twente
- Vrije Universiteit Amsterdam
- Wageningen University & Research
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, electrical engineering or simila,r with an affection for machine learning; You are an independent and original thinker with a creative mindset; You are a fast thinker with excellent analytical and
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engineering Engineering » Simulation engineering Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 5 Feb 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Not
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journals and present at international conferences. Where to apply Website https://www.academictransfer.com/en/jobs/357570/phd-on-mixed-signal-circuit-des… Requirements Specific Requirements A master’s degree
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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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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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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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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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). 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
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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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physical). Solid background in programming and experience with machine learning. Knowledge of participatory design and co-creation methodologies. Ability to learn independently and passion for research