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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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, traditional planning often fails to capture workload variability, uncertainty, and the complex interaction between product features, labor availability, and machine capacity. Your PhD will address
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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
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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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bringing together a diverse team of PhD candidates who will focus on three key areas: Probabilistic and differentiable algorithms for machine learning; Programming language implementation for high
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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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the main deliverables to the consortium before 31 December 2028. In the remaining time of this position, the PhD candidate can write and finalize the thesis. Where to apply Website https
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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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, neuroimaging and clinical psychiatry, with direct clinical impact. Your main activities are: analyzing and integrating multimodal MRI data for biotype identification; applying machine learning and advanced
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of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PhD candidate who is eager to work on AI based solutions for predictive inteligence for MRI scanning. The candidate will