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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
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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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limited. To learn from past warmer climates and better understand the link between climate and extremes, we can use proxy-based climate reconstructions and climate models for past warmer climates. However
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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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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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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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of the future, striving to improve healthcare and society as a whole. Where to apply Website https://www.academictransfer.com/en/jobs/356668/phd-in-machine-learning-for-dru… Requirements Specific Requirements
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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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with machine learning. Self-Driving Laboratories (SDLs) are emerging research environments where experiments are planned, executed, and analyzed in closed-loop workflows that combine automated