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Field
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cell (and one cell–cell interaction) at a time. You will work with large-scale single-cell and spatial transcriptomics data to develop and apply single-cell foundation models — generative machine
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models
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cell (and one cell–cell interaction) at a time. You will work with large-scale single-cell and spatial transcriptomics data to develop and apply single-cell foundation models — generative machine
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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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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-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to