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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome
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learning models (e.g., multimodal AI, large language/world models) with specific finetuning for ELEVATE; designing geographically context-sensitive urban design recommendations that promote active mobility
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done for two case studies, namely Western Scheldt and Wadden Sea. To facilitate knowledge exchange and the learning process, interactive workshops will be organized on location (Western Scheldt and
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writing; Strong communication skills and preferably expertise with learning design (for example visual media production project); Knowledge of mediated participation in a context of natural resource
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Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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to generalise, and their susceptibility to known issues related to synthetic data training, such as model collapse. As a postdoctoral researcher on this project, you will: Design and evaluate methods
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programme investigating the relationship between pupil dynamics, visual attention and cognitive processing. Your responsibilities include: designing, programming and conducting behavioural and pupillometry