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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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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation 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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meaningful learning be redefined in today’s AI-enabled educational landscape? The project has the following overarching goals: (1) develop a theoretical framework of constructive alignment for AI-rich teaching
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between Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; multiple courses on topics such as leadership for academic staff; multiple courses on topics such as time
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and apply AI and machine learning methods for signal processing, image analysis, data fusion, and prediction; · build physics-informed and hybrid AI models that combine geophysical knowledge with data
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benefits: 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; multiple courses on topics
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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research skills, and experience building reproducible analysis or simulation workflows; have demonstrable expertise in one of the following domains and the motivation to acquire working knowledge