225 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" PhD scholarships in Netherlands
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- University of Amsterdam (UvA); Published yesterday
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clinical diagnostics. Where to apply Website https://www.academictransfer.com/en/jobs/357934/phd-position-on-enriching-digit… Requirements Specific Requirements We are seeking a highly motivated researcher
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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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to the collaborative nature of the project, regular physical presence is expected Where to apply Website https://www.academictransfer.com/en/jobs/358284/phd-position-just-production-ar… Requirements Specific
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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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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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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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research to address climate justice. JUST ART PhDs will study and develop concrete cases to learn how art and artistic research can be embedded in ongoing and emerging work on climate justice. They will
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on development of tools for i) improved imaging, ii) characterization of metabolism, and iii) monitoring of protective and -restorative therapies. Where to apply Website https://www.academictransfer.com/en/jobs
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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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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