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CDT in Machine Learning Systems About the CDT Machine Learning has a dramatic impact on our daily lives built on the back of improved computer systems. Systems research and ML research are symbiotic
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with disabilities through inclusive Human-Computer Interaction (HCI) and participatory design methods. This PhD project will focus on methods and experiences in co-designing with stakeholders, especially
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the EAA and ensuring equal participation in society. The ideal candidate will have a strong interest in accessibility, design methods, human-computer interaction, artificial intelligence, and human-centered
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(based solely on DL) with XAI, to incorporate subtle human judgement, thus aiming to bring robotic systems closer to the thresholds of human cognition Designing XAI-based methods in robotics and computer
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from on-board and off-board sensors have been collected and analysed. However, such raw data collected from multiple sensors is often disturbed and does not indicate the ship performance at calm water
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skills. Preferred Qualifications: Prior research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization
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of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to invent, develop and evaluate novel methods for pre-training and fine-tuning of perceptual foundation
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edge applications. Tasks and responsibilities: Develop new deep learning, computer vision and multimodal learning methods for pre-training and fine-tuning perceptual foundation models. Actively
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. Its relatively recent establishment in the large peri-alpine lakes, such as Lake Geneva, combined with its high colonization potential, raises multiple ecological and economic concerns. Ecologically
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uncertainty characterisations for mass balance estimates from satellite altimetry applying the results in comparative studies and combination studies with complementary methods such as satellite gravimetry