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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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under global change requires accurate and consistent soil information at global scale. Current global soil maps are derived using empirical machine learning that often ignores known soil processes
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& Spatial Planning, Physical Geography, and Sustainable Development. The team of the Department of Physical Geography excels in research and education on BSc, MSc and PhD level. We research processes
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their added value against simpler machine-learning baselines; train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies
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against simpler machine-learning baselines; • train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies and
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closely with colleagues and students, you’ll apply mathematics to tackle societal challenges and bring new data-driven modelling, experiment, and computer-aided mathematics into education. We welcome
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and students, you’ll apply mathematics to tackle societal challenges and bring new data-driven modelling, experiment, and computer-aided mathematics into education. We welcome applicants from all areas
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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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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, physiology and disease development; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting