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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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). 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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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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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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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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., high performance, functional array programming DSLs) to tackle challenging probabilistic and differentiable programming applications (e.g., experimental design, machine learning for science). We do so by
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning