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that prioritise student learning in the age of AI. Job requirements You hold a PhD in Educational Science, Learning Sciences, Social and Behavioural Sciences, Engineering Education, Human-Computer Interaction or a
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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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requirements For this postdoctoral role, we are looking for someone with a demonstrated background in SAR processing, SAR applications in agriculture and machine learning: A PhD in Earth Observation, Geodesy
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machine learning for molecular and material design; quantum computing for bioinformatics; quantum approaches for safe and sustainable molecular design; and benchmarking quantum simulations of materials
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researcher who enjoys developing methods for complex biological data and working closely with experimental scientists. You meet the following criteria: a PhD, or a PhD close to completion, in machine learning
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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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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requirements We will consider applications based on the following skillsets. If you do not fullfil all requirements, please still apply (there is room for learning-on-the-job). A PhD in aerospace/mechanical
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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