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professional development. For more information, please visit Working at Utrecht University external link . About us A better future for everyone. This ambition motivates our scientists in executing their leading
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), using large-scale real-world data. This PostDoc position is part of a ZonMw-funded project on long-term risks in patients with post-COVID compared to individuals with similar complaints after other viral
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data. You will also develop explainable AI (XAI) techniques to link imaging features and molecular markers to clinically meaningful outcomes, ensuring both predictive accuracy and medical
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diagnoses, symptoms, signs, and disease durations linked to infections and post-infection syndromes including post-COVID. You will work with extensive real-world data from general practitioners to extract
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now includes five years of follow-up data. You will focus on linking the molecular data with clinical data, which is being analyzed by clinical researchers. Additionally, you may integrate imaging data
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geographic and taxonomic scope of the analysis, develop approaches to link plant distribution data to (historic) urbanization data, perform data analyses and visualizations to reveal historic trends in species
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to continue to invest in their growth. For more information, please visit Working at Utrecht University external link . About us A better future for everyone. This ambition motivates our scientists in executing
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in their growth. For more information, please visit Working at Utrecht University external link . About us A better future for everyone. This ambition motivates our scientists in executing
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to creativity in AVT in different translation modalities, define whether user attention and preference is linked to creativity, and gather data on how errors affect user attention. You will use a mixed method
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convolutional and transformer-based architectures, as well as methods for modeling temporal dynamics in longitudinal imaging and omics data. You will also develop explainable AI (XAI) techniques to link imaging