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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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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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develop models that can read, extract and acquire knowledge from legacy data, coming both in the form of text and in the form of structured data (e.g. physical measurements) to predict characteristics
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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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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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learning has strong potential for computer vision, from hyperbolic image segmentation [2] to hyperbolic tree embeddings [3] and hyperbolic vision-language models [4,5]. [1] Nickel, Maximillian, and Douwe
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, particularly integer programming, e.g., vehicle routing and packing problems and heuristics; simulation; data-driven modelling; decision support systems; AI (reinforcement learning, machine learning). Motivation
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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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the Computer Engineering group. Curious to learn more about the project, and perhaps our group? Feel free to browse our webpages: About our department: QCE department . About our group: Computer Engineering Lab