456 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" PhD positions in Netherlands
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- Delft University of Technology (TU Delft)
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- Delft University of Technology (TU Delft); yesterday published
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- DIFFER; Published yesterday
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- Delft University of Technology (TU Delft); Published today
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- Eindhoven University of Technology (TU/e); Published yesterday
- Erasmus MC (University Medical Center Rotterdam); yesterday published
- Erasmus University Rotterdam (EUR); yesterday published
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- University Medical Center Utrecht (UMC Utrecht); Published 31 Oct ’25
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- University of Amsterdam (UvA); 10 Oct ’25 published
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- University of Amsterdam (UvA); Published 28 Nov ’25
- University of Twente (UT); today published
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- Utrecht University; Published yesterday
- Vrije Universiteit Amsterdam
- Vrije Universiteit Amsterdam (VU); Published 28 Nov ’25
- Vrije Universiteit Amsterdam (VU); Published 31 Oct ’25
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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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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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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
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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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and scalable. Design and build a technology demonstrator prototype of clinical-testing grade. Collaborate with interdisciplinary teams, including clinicians, engineers, and machine learning (ML) and
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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of related events and activities. In addition, they may be given a temporary appointment at the University to design and teach an undergraduate course in the general area of their interest. Requirements A
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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We’re seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon