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environments; (2) identify which patterns of student-AI interactions influence the adoption of deep or surface approaches to learning; (3) create, implement and evaluate guidelines and knowledge base
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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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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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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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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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: Within this international project, TU Delft will develop a machine learning-based forward operator to enable the assimilation of SAR imagery into the crop growth model. You will: Process SAR imagery over
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Specific Requirements The ideal candidate has the following qualifications: - interest in human cognition - experience with neural data, especially EEG or MEG - experience with machine learning models
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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
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, pumps, valves operated by a computer and the corresponding software Develop flow cells to connect various spectroscopic tools to the setup Create and validate reproducible automated workflows