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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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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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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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; 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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for reasoning, on efficient and explainable machine learning for extracting and structuring information from large datasets, and on combining the two in neuro-symbolic AI.
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facilities and access to microscopes etc. The BioMS group is world-renown for mass spectrometry-based proteomics and structural biology, and applies these technologies to study amongst others cancer, auto
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and authorities interested in improving civic participation. The research in this postdoctoral position focuses on text and image feature learning for news ecosystems, analysing the complex
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facilities and access to microscopes etc. The BioMS group is world-renown for mass spectrometry-based proteomics and structural biology, and applies these technologies to study amongst others cancer, auto
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completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development and/or fine-tuning. A practical mindset
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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