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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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)design; integrating multi-source datasets, including street-view imagery, GIS data, smartphone mobility data, and qualitative insights; applying and adapting state-of-the-art foundational AI and machine
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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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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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postdoc candidate, with a keen interest in academic and professional development, who meets the following requirements: a PhD degree in computer science or artificial intelligence; demonstrable machine
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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